A LOW-THRUST TRANSFER STRATEGY TO EARTH-MOON COLLINEAR LIBRATION POINT ORBITS
A Thesis
Submitted to the Faculty
of
Purdue University
by
Martin T. Ozimek
In Partial Fulfillment of the
Requirements for the Degree
of
Master of Science
December 2006
Purdue University
West Lafayette, IN
ii
For Mom, Dad, Sarah, and Bops
iii
ACKNOWLEDGMENTS
I would like to thank my parents for their seemingly never ending support and
confidence that I would persevere in my personal quest for an advanced degree. The
decision to commit to a higher degree has been an adventure that I hope to continue
along, and I can’t begin to explain the importance of that priceless feeling of simply
knowing that someone is there when needed. Professor Kathleen Howell, my advisor, is
also owed a great deal of gratitude, not only for posing the fateful words “low-thrust” one
day in her office during a discussion about NASA’s potential Jupiter Icy Moons missions,
but also for her personal standard for excellence that she instills in each of her many
successful graduate students. I’ve always felt that Purdue University reached out to me
and offered me that “extra” indefinable something from the moment I began seriously
considering a graduate institution. In no other person is this ambiguous something
“extra” exemplified than in Professor Howell, who has sought to ensure that my research
efforts are guided and ultimately shared with others in the best possible way. This notion
has also been exemplified by Professor James Longuski, whose door has always been
open to me from day one, and whom I must also credit in heavily influencing my
decision to attend Purdue University. On more than one occasion, Professor William
Crossley has also had his door open to help along my path of solving what turned out to
be a difficult optimization problem.
I also owe many thanks to Daniel Grebow. Dan has been a close friend, colleague, and
even roommate throughout my stay at Purdue, and this work is the direct continuation of
a mission analysis that we worked on together. Often, many of the new ideas I have for
current and future research are a result of simple dialogs that we frequently engage in.
iv
The idea to study mission applications toward lunar south pole coverage would never
have originated had I not fortuitously been privileged to work at the NASA Goddard
Spaceflight Center during the summers of 2005 (as a member of the NASA Academy by
way of the Indiana Space Grant Consortium) and 2006. There, I benefited from the
knowledge of some of the greatest libration point mission experts in the world, and owe
particular thanks to my mentor David Folta. Support from NASA under contract
numbers NNG05GM76G and NNX06AC22G is greatly appreciated. Finally, I would
like to thank Purdue University for financial support, including the Andrews Fellowship,
for the entirety of my M.S. program.
v
TABLE OF CONTENTS
Page
LIST OF TABLES............................................................................................................ vii
LIST OF FIGURES ......................................................................................................... viii
ABSTRACT........................................................................................................................ x
1 INTRODUCTION ............................................................................................................1
1.1 Historical Overview of the Three-Body Problem.......................................................3
1.2 Developments in Low-Thrust Transfer Trajectories ..................................................5
1.2.1 Optimal Control .................................................................................................. 5
1.2.2 Application to Orbit Problems ............................................................................ 6
1.3 Focus of this Work .....................................................................................................7
2 BACKGROUND ............................................................................................................10
2.1 The Circular Restricted Three-Body Problem..........................................................10
2.1.1 Assumptions...................................................................................................... 11
2.1.2 Geometry........................................................................................................... 11
2.1.3 Equations of Motion.......................................................................................... 12
2.1.4 Libration Points................................................................................................. 15
2.1.5 Formulation Relative to P2 in the CR3BP......................................................... 17
2.2 Natural Periodic Orbits in the CR3BP......................................................................18
2.2.1 First-Order Variational Equations Relative to the Collinear Points.................. 19
2.2.2 The State Transition Matrix .............................................................................. 23
2.2.3 The Fundamental Targeting Relationships ....................................................... 25
2.2.4 Periodic Orbits .................................................................................................. 28
2.3 Invariant Manifolds ..................................................................................................31
2.3.1 Stable and Unstable Manifolds Associated with the Collinear Points.............. 32
vi
Page
2.3.2 Invariant Manifolds Relative to a Fixed Point.................................................. 35
2.3.3 Computation of Manifolds Corresponding to Fixed Points Along an Orbit ..... 37
2.4 Optimal Control Theory ...........................................................................................39
2.4.1 Summary of the First Necessary Conditions for Optimal Control.................... 40
2.4.2 Tests for a Local Minimum Value of the Performance Index .......................... 43
3 LOW-THRUST TRANSFER ALGORITHM ................................................................45
3.1 Engine Model ...........................................................................................................46
3.2 Control Law Derivation............................................................................................48
3.3 Adjoint Control Transformation...............................................................................55
3.4 Numerical Solution via Direct Shooting: A Local Approach...................................59
3.5 Shotgun Method for Initial Conditions: A Global Approach...................................63
4 MISSION APPLICATIONS...........................................................................................66
4.1 Orbits for Line-of-Sight Lunar South Pole Coverage (CR3BP) ..............................67
4.1.1 Three-Dimensional Periodic Orbits in the CR3BP ........................................... 67
4.1.2 Families of Orbits for Lunar South Pole Coverage ........................................ 68
4.1.3 Mission Orbit Selection Criteria ....................................................................... 73
4.2 Optimal Transfers to the Earth-Moon Stable Manifold............................................75
4.2.1 Transfers to a 12-Day L1 Halo Orbit ................................................................. 78
4.2.2 Transfer to a 14-Day L1 Vertical Orbit ............................................................. 87
4.2.3 Transfer to a 14-Day L2 Butterfly Orbit ............................................................ 91
5 SUMMARY AND RECOMMENDATIONS.................................................................96
5.1 Summary...................................................................................................................96
5.2 Recommendations for Future Work .........................................................................98
LIST OF REFERENCES...................................................................................................99
vii
LIST OF TABLES
Table Page
Table 4.1 Dynamical and Propulsion Constants................................................................77
Table 4.2 12-Day L1 Halo Orbit Transfer Data Summary .................................................82
Table 4.3 12-Day L1 Halo Orbit Long Transfer Data Summary........................................86
Table 4.4 14-Day L1 Vertical Orbit Transfer Data Summary............................................91
Table 4.5 14-Day L2 Butterfly Orbit Transfer Data Summary ..........................................95
viii
LIST OF FIGURES
Figure Page
Figure 2.1 Geometry in the Restricted Three-Body Problem............................................12
Figure 2.2 Equilibrium Point Locations for the CR3BP....................................................16
Figure 2.3 Geometry of P2-Centered Rotating Frame .......................................................18
Figure 2.4 Linearized L1 Periodic Orbit.............................................................................23
Figure 2.5 Basic Diagram for a Free Final Time Targeting Scheme.................................27
Figure 2.6 Targeting a Perpendicular X-axis Crossing in the CR3BP ..............................30
Figure 2.7 Several L1 Lyapunov Orbits Obtained Via Continuation.................................31
Figure 2.8 Stable and Unstable Manifold at eqX
...............................................................34
Figure 2.9 Global Manifolds for an Earth-Moon L1 Lyapunov Orbit, Ay = 23,700 km.....38
Figure 3.1 CSI Engine Example – Smart-1 Ion Engine.....................................................46
Figure 3.2 VSI Engine Example - VaSIMR Rocket ..........................................................47
Figure 3.3 Behavior of θΜ and τM Along the Stable Manifold Tube.................................51
Figure 3.4 Velocity Reference Frame................................................................................58
Figure 3.5 Numerical Algorithm – Direct Shooting Method via SQP ..............................62
Figure 3.6 Example Population Parameters for a 12-Day Halo Orbit ...............................65
Figure 4.1 Southern Halo Orbit Families: Earth-Moon L1 (Orange) and L2 (Blue); Moon
Centered, Rotating Reference Frame ................................................................70
Figure 4.2 Vertical Orbit Family of Interest: Earth-Moon L1 (Magenta) and L2 (Cyan);
Moon Centered, Rotating Reference Frame......................................................71
Figure 4.3 Southern L2 Butterfly Orbit Family; Moon Centered, Rotating Reference
Frame.................................................................................................................72
Figure 4.4 Period versus Maximum x-Distance from the Moon (Left); Definition of
Maximum x-Distance (Right) ...........................................................................74
ix
Figure Page
Figure 4.5 Stability Index versus Maximum x-Distance from the Moon ..........................75
Figure 4.6 Optimal Orbit Raising from LEO.....................................................................77
Figure 4.7 Stable Manifold Tube for 12-Day L1 Halo Orbit (Green) and Target Reference
Trajectory Along the Manifold (Blue) ..............................................................79
Figure 4.8 Low-Thrust Short Transfer to a 12-Day L1 Halo Orbit ....................................80
Figure 4.9 Position and Velocity Costate Time Histories for the 12-Day L1 Halo Orbit
Transfer .............................................................................................................81
Figure 4.10 Time History of Propulsion Related Parameters for the 12-Day L1 Halo Orbit
Transfer .............................................................................................................82
Figure 4.11 Stable Manifold Tube for 12-Day L1 Halo (Green) and Initial Target
Reference Trajectory Along the Manifold (Blue) for Long Transfer ...............83
Figure 4.12 Low-Thrust Long Transfer to a 12-Day L1 Halo Orbit ..................................84
Figure 4.13 Position and Velocity Costate Time Histories for the 12-Day L1 Halo Orbit
Transfer .............................................................................................................85
Figure 4.14 Time History of Propulsion Related Parameters for the 12-Day L1 Halo Orbit
Long Transfer....................................................................................................86
Figure 4.15 Stable Manifold Tube for 14-Day L1 Vertical Orbit (Green) and Initial Target
Reference Trajectory Along the Manifold (Blue).............................................88
Figure 4.16 Low-Thrust Transfer to a 14-Day L1 Vertical Orbit.......................................89
Figure 4.17 Position and Velocity Costate Time Histories for the 14-Day L1 Vertical
Orbit Transfer....................................................................................................90
Figure 4.18 Time History of Propulsion Related Parameters for the ................................90
Figure 4.19 Stable Manifold Tube for 14-Day L2 Butterfly Orbit (Green) and Initial
Target Reference Trajectory Along the Manifold (Blue) .................................92
Figure 4.20 Low-Thrust Transfer to a 14-Day L2 Butterfly Orbit .....................................93
Figure 4.21 Position and Velocity Costate Time Histories for the 14-Day L2 Butterfly
Orbit Transfer....................................................................................................94
Figure 4.22 Time History of Propulsion Related Parameters for the 14-Day L2 Butterfly
Orbit Transfer....................................................................................................94
x
ABSTRACT
Ozimek, Martin T. M.S.A.A., Purdue University, December, 2006. A Low-Thrust Transfer Strategy to Earth-Moon Collinear Libration Point Orbits. Major Professor: Kathleen Howell. A strategy to compute low-thrust transfer trajectories in the Earth-moon circular
restricted three-body problem is developed. The dynamical model is formulated
assuming variable specific impulse engines, an advanced finite-thrust propulsion model.
Originating in an Earth parking orbit, the spacecraft is delivered to a location along the
stable manifold; the engines power off and the spacecraft asymptotically approaches the
periodic libration point orbit of interest. Elements of optimal control theory are used to
derive a primer vector control law as well as a set of additional dependent variables that
characterize the solution to the corresponding two-point boundary-value problem
(TPBVP). A hybrid direct/indirect method results in transfer trajectories that are
associated with locally minimal propellant consumption. The generation of useful initial
conditions is aided by an adjoint control transformation and a global “shotgun” method.
The solution strategy is demonstrated in a detailed development of transfers to a 12-day
L1 halo orbit, a 14-day L1 vertical orbit, and a 14-day L2 “butterfly” orbit. These target
orbits are all selected from the families that meet line-of-sight coverage requirements in
support of lunar south pole mission architecture.
1
1. INTRODUCTION
As understanding of the solar system and the dynamical structure of the space
environment increases, ever more complex questions continue to emerge. In the past 60
years, access to space has opened to both human-crewed and robotic spacecraft. Both
scientific interest and engineering capability have invariably played a vital role in this
expansion of knowledge. New scientific demands often spur engineering advancements
to accomplish a set of mission objectives; breakthroughs in engineering capability
enlighten the scientific community and serve as a catalyst for original mission concepts.
Perhaps, as a consequence, it is not surprising that libration point orbits have relatively
recently risen as venues for robotic spaceflight. Beginning with NASA’s 1978 solar wind
measuring satellite, the International Sun-Earth Explorer (ISEE-3) [1], libration point
orbits are now considered viable options to meet a range of scientific goals. Although
originally proposed for manned Apollo missions [1], such orbits were not exploited prior
to ISEE-3. However, with the ever increasing speed of computers, trajectory design
within the context of the n-body problem is now feasible. Although the n-body problem
is unsolvable in closed form, certain simplifying assumptions expose equilibrium
solutions, i.e., the libration points. Periodic orbits in their vicinity can be computed
numerically. Not coincidentally, the success of the early missions like ISEE-3 and the
continuing increase in computational capabilities, have led to more contemporary
libration point missions such as WIND [2], SOHO [3], ACE [4], MAP [5], and Genesis
[6]. Clearly, such trajectory designs fill a particular niche in mission applications where
long-duration scientific observation is required.
More recently, a geometrical approach in studies of the multi-body problem has also
led to alternative strategies for transfer trajectory design, as well as stationkeeping
maneuvers. This approach is based on a complete analysis of the phase space in the
2
neighborhood of the periodic libration point orbits. In-depth analysis of the phase space,
as suggested by Poincaré [7] in 1892, has evolved into dynamical systems theory (DST).
An important astrodynamics application from DST is the exploitation of invariant
manifolds to design trajectory arcs that asymptotically arrive at, and depart from, the
vicinity of the periodic libration point orbits. Propagation of these manifolds often yields
very efficient transfers. In some cases, these manifolds even pass within the vicinity of a
planet [6]. Typically, such transfer arcs are more applicable to robotic spaceflight, since
an additional time-penalty is often incurred. This DST approach was a key component in
the design of the Genesis low-energy trajectory. The Genesis trajectory design
incorporated heteroclinic and homoclinic arcs to deliver a spacecraft to a Sun-Earth L1
libration point orbit with a subsequent return to Earth [6].
Such manifold transfer trajectories are also useful for applications in the Earth-moon
system. Recent studies have identified libration point orbits as a potential component in
the development of a communications relay between a manned facility at the lunar south
pole and ground stations on the Earth. In the Earth-moon problem, however, many of the
viable libration point orbits possess manifolds that pass no closer than 50,000 km to the
Earth. These manifolds may still serve as transfers, but additional fuel is necessary to
incorporate a leg from an Earth parking orbit to the manifold.
One type of intermediate transfer from the parking orbit to the manifold involves the
use of low-thrust propulsion. Low-thrust propulsion introduces a time penalty, but can
yield lower fuel expenditure due to higher specific impulse engines. Incorporating low-
thrust also adds dynamical complexity because a “steering” law for the thrust direction
and magnitude must be determined and successfully implemented. The objective of the
current work is a method to design low-thrust transfers that deliver a vehicle onto a
manifold trajectory. The specific application of interest supports the establishment of
lunar south pole communications relay infrastructure.
3
1.1 Historical Overview of the Three-Body Problem
The general problem of three bodies was first investigated by Isaac Newton in his 1687
landmark work, the Principia [8]. His successor, Leonhard Euler, receives much of the
credit, however, for formulation of the restricted problem of three bodies. In 1765, Euler
identified the equilibrium solutions in the restricted problem, i.e., the collinear libration
points L1, L2, and L3 [9]. Euler also introduced a synodic reference frame in connection
with the motion of the moon in 1772 [9]. Later, in 1772, the same year that Euler
formulated the restricted problem, Lagrange determined the locations of two additional
equilibrium points, the triangular libration points, L4 and L5 [9]. Euler’s formulation
allows only one integral of motion in the restricted problem as determined by Jacobi in
1836, by balancing energy and angular momentum [10]. In 1878, George William Hill
published his Researches in Lunar Theory [11], effectively modeling the motion of the
moon as a satellite, exposed to the gravitational field of the Earth and the perturbing force
of the Sun.
In 1899, Henri Poincaré published his three-volume work, Les Méthodes Nouvelles de
la Mécanique Celeste [7], the result of his unparalleled response to a contest in 1887.
Participants were challenged to produce a definitive solution to the n-body problem.
Ironically, Poincaré eventually won the prize by proving that the n-body problem cannot
be solved in closed form. His work is highly regarded for the qualitative emphasis on
behaviors in the n-body problem. In particular, Poincaré focused on the behavior of
trajectories as time goes to infinity. Of course, the only trajectory that can be defined at
infinite time is a periodic orbit. A detailed analysis of the phase space of a non-integrable
system led Poincaré to invent a technique known as the “surface of section”. This work
is considered the foundation of dynamical systems theory. In addition, Poincaré also
proved that Jacobi’s Constant is the only integral of the motion in the restricted problem.
Poincaré’s work generated great interest in the following decades. Periodic orbits are a
common topic noted in the early 20th century work of Darwin [12], Plummer [13], and
Moulton [14]. In the absence of extensive computational capabilities, these researchers
exploited expansion procedures to construct analytical approximations. Darwin and
4
Plummer are noted for approximating planar periodic orbits in the restricted problem.
Beyond a general focus on planar periodic orbits, Moulton also studied in-plane and out-
of-plane orbits in the vicinity of the collinear libration points.
The renewed attention on the restricted problem in the latter half of the 20th century is
attributed primarily to the emergence of high-speed computing and the beginning of the
space age. Several developments are notable. Szebehely’s 1967 book The Theory of
Orbits [10], a comprehensive text on the three-body problem, is still regarded as one of
the most thorough sources of information on the problem of three bodies. By the 1960’s,
in support of the Apollo missions to the moon, trajectories computed in the restricted
three-body problem were in development. Farquhar coined the term “halo” orbits and
developed analytical approximations of these three-dimensional periodic orbits in the
vicinity of L2 in the Earth-moon system. In addition to proposing orbits for use in the
Apollo missions and ultimately for the unmanned ISEE-3 [1] spacecraft in the Sun-Earth
system, Farquhar and Kamel [15] also developed third-order approximations for quasi-
periodic orbits. Richardson and Cary [16] expanded these approximations to fourth
order. Breakwell and Brown [17] are noted for a numerical study that generates families
of periodic halo orbits. Howell [18] extended the analysis to include all collinear points
and the families over all three-body systems. Hénon [19] has also produced thorough
analyses of periodic orbits, including vertical orbits. Approximations for nearly
rectilinear halo orbits at the collinear points were produced by Howell and Breakwell
[20]. Perhaps the most rigorous numerical generation of periodic orbits is the
investigation by Dichmann, Doedel, and Paffenroth [21], that uses the software package
AUTO to detail periodic orbits as well as their interrelated orbits via bifurcation.
5
1.2 Developments in Low-Thrust Transfer Trajectories
1.2.1 Optimal Control
The development of finite-burn trajectories (in particular, when the thrust level is low)
is an application of optimal control theory and the calculus of variations. Optimization of
curves and points can be traced to the 1600’s, when the calculus of variations was first
introduced as an analysis tool for minimizing functions of functions, or “functionals”. It
received particular attention with Johann Bernoulli’s proposal of the brachistochrone
problem to the scientific community 1696 [22]. Generally, the focus is the set of
conditions on the functions that drive the functional to a maximum or minimum. Such
functions are termed “extremals”. In 1755, Joseph Lagrange wrote a letter to Leonhard
Euler in connection with their mutual interest in an analytical solution to the tautochrone
problem [23]. The analytical development resulted in the formulation of the Euler-
Lagrange Equations (and the corresponding transversality condition). These equations
have since served as the basis of a widely known technique for determining extremals,
and a foundation of the calculus of variations, a term created by Euler in 1766.
Classification of these extremals is accomplished via the Legendre-Clebsch necessary
condition, the Weierstrass Condition, and in the most general of terms, Pontryagin’s
Minimum Principle. Details of the generalized theory in support of the applications of
this methodology appear in Bryson and Ho [24], Hull [25], and Kirk [26].
Low levels of thrust in the computation of finite-burn spacecraft trajectories is
achieved by the seemingly parallel availability of high-speed computing and the
continuing development of advanced propulsion systems. In his 1963 book, Optimal
Spacecraft Trajectories [27], Lawden used primer vector theory to outline a general
procedure for determining optimal low-thrust trajectories. Primer vector theory blends a
control law and switching structure common in many “indirect” optimization methods.
Indirect, low-thrust, trajectory optimization methods are typically characterized by the
two-point boundary-value problem formulation from optimal control theory, with a
continuous parameterization of the thrust direction via the tangent to the primer vector.
6
Such approaches are termed “indirect” because once the two-point boundary-value
problem is established, no minimization is required on the cost functional directly
(although it can be included [28-30]). Alternatively, the solution involves a root-solving
process on the kinematical as well as the natural boundary conditions as a result of the
Euler-Lagrange equations, transversality condition, and a corresponding secondary test
for a minimum. Conversely, “direct” methods involve an attempt to minimize the cost
functional itself, and often use many variables to parameterize the thrust magnitude and
direction. A common parameterization of the thrust direction is the development of a
spline function [29,31]. While indirect methods typically require more precise initial
conditions due to numerical sensitivities, the lower dimension on the search vector
implies fewer computations. Marec [32] offers further mathematical analysis of the
primer vector; examines high- and low-thrust propulsion systems and details the
Contenson-Pontryagin Maximum Principle. Early applications to impulsive rendezvous
problems are available in Jezewski [33] and to low-thrust rendezvous in Melbourne and
Sauer [34].
Further increases in computing speed have allowed more sophisticated methods in
numerical solutions to optimization problems. All trajectory optimization problems are
typically solved with the following numerical methods: direct shooting, indirect
shooting, multiple shooting, direct transcription, indirect transcription, dynamic
programming, or genetic algorithms. For a detailed survey of all of these different
methods, see Betts [35]. Thus, optimal control theory serves to set up the conditions and
constraints that must be met to determine the existence of an optimal control, and the
numerical optimization methods serve as the means to actually compute solutions that
meet these exact conditions.
1.2.2 Application to Orbit Problems
A number of investigations are notable in examining optimal low-thrust trajectories.
Many applications are formulated in the two-body problem [36-40]. In the three-body
problem, there has been less attention. In one example, Herman and Conway [41]
7
compute optimal, low-thrust, Earth-moon transfers using equinoctial elements, with a
direct collocation solution method. Kluever [29-30] also develops Earth-moon transfers,
but utilizes a hybrid direct/indirect method. Golan and Breakwell [42] use matching of
two trajectory segments to transfer into lunar orbit; transfers to L4 and L5 are also
presented. In a Hill formulation of the three-body problem, Sukhanov and Eismont [43]
establish a primer vector control law to develop a three-dimensional transfer into a Sun-
Earth L1 halo orbit that includes a constrained thrust direction. Although Seywald,
Roithmeyer, and Troutman [44] do not employ the circular restricted three-body
equations of motion, they are notable for attempting to provide approximate analytical
solutions of circle-to-circle orbit transfers with a variable specific impulse engine, and,
furthermore, compare the engine model with results using constant specific impulse
engines. Primer vector theory is employed by Russell [45] to develop a global search and
local optimization method and establish a Pareto front on his resulting fixed time
solutions. Russell subsequently applies his method to produce transfers to planar Earth-
moon and Sun-Earth distant retrograde orbits. Senent, Ocampo and Capella [28] also use
primer vector theory for free final time transfers to Sun-Earth libration point orbits via the
stable manifold.
1.3 Focus of this Work
Optimal, free final time low-thrust transfer trajectory profiles to several Earth-moon
libration point orbits is the objective of this work. The target orbits are selected based on
lunar south pole coverage applications, including halo orbits, vertical orbits, and
“butterfly” orbits. These transfer trajectories represent an extension of the exhaustive
coverage analysis by Grebow et al. [46].
8
The work is organized as follows.
Chapter 2:
All background material is presented in Chapter 2. The equations of motion that
govern the nondimensional, barycentric, cartesian, circular restricted three-body problem
are developed. A moon-centered model is also introduced. The linear variational
equations that result from using the collinear libration points as a reference are
introduced, and, thus, form the basis for generating initial conditions in the nonlinear
problem. Then, an alternate set of linear variational equations is developed, where the
time-varying orbital trajectory is exploited as a reference. The second set of variational
equations is useful for iterative orbit targeting. Invariant manifold theory is summarized
for both libration points and for fixed points along a periodic libration point orbit.
Finally, the necessary conditions for establishing a stationary value of a generalized
performance index are introduced. These include both the Euler-Lagrange equations and
Pontryagin’s Minimum Principle. Such conditions ultimately yield the full two-point
boundary-value problem that may be solved via nonlinear programming methods.
Chapter 3:
Optimal control theory is applied to develop the well-known primer vector control law
parameterization. Variable specific impulse engines (e.g., those in development for the
VaSIMR rocket project) are modeled and produce notable improvement in numerical
convergence. The problem is formulated as a free final time transfer from an Earth
parking orbit to the stable manifold associated with the libration point orbit of interest.
Using the stable manifold allows a stopping condition, and a secondary coast phase; the
spacecraft asymptotically converges to the desired orbit. Once the control law and the
elements of the two-point boundary-value problem are established, a solution method is
presented. This scheme incorporates a “global” search method, and a subsequent local
sequential quadratic programming (SQP) algorithm. Rather than solve the complete two-
point boundary-value problem, the local approach is set up as a direct shooting method,
and is termed a hybrid direct/indirect approach. The adjoint control transformation
(ACT) is used to map the initial costates into more meaningful physical quantities that are
useful in the global method and the first step of the local, direct shooting method.
9
Chapter 4:
Once the methodology is established, the criteria for target orbit selection is
summarized and the orbit families of interest are introduced. A halo orbit, vertical orbit,
and a “butterfly” orbit are selected and the solution procedure is applied to generate low-
thrust transfer trajectories. The resulting thrust profiles, trajectory plots, and overall
performance parameters are then discussed.
Chapter 5:
Conclusions concerning the solution methodology are presented. Then,
recommendations on future work, including different solution methods, equations of
motion, and higher fidelity models are detailed. Finally, potential future applications as a
result of this work are presented.
10
2. BACKGROUND
Some fundamental mathematical tools and concepts are necessary for the development
of the trajectories and transfers in the current application. The cartesian, nondimensional,
barycentric Circular Restricted Three-Body Problem (CR3BP) is initially formulated, and
the corresponding dynamical equations of motion are derived. The five equilibrium
points are the basis for the computation of special periodic orbits. The state transition
matrix is introduced as a tool for predicting linear, and approximating nonlinear, motion.
An alternate formulation of the equations of motion, centered at the smaller primary, is
also presented. The fundamental structure underlying the dynamics in the restricted
problem is analyzed with invariant manifold theory, yielding natural pathways to and
from a periodic orbit. Basic numerical methods for a targeting procedure are detailed,
forming the groundwork for determining periodic orbits. Finally, the basic concepts of
optimal control theory are introduced, including the necessary conditions along a
trajectory for the existence of a locally optimal transfer trajectory.
2.1 The Circular Restricted Three-Body Problem
The rapid growth of high-speed computing in the last three decades has helped spark
the discovery of new and exciting trajectories. Many of these trajectories exist within the
context of the Circular Restricted Three-Body Problem (CR3BP). An exact analytical
solution to the CR3BP does not exist; thus, any solutions beyond the equilibrium points
require numerical integration. Nevertheless, at the expense of additional numerical
exploration, propagation of trajectories in this model result in non-Keplerian orbital
motion, such as “figure-eight” orbits, “halo” orbits, and an infinite variety of other
periodic orbits; quasi-periodic trajectories have also been identified.
11
2.1.1 Assumptions
Given an arbitrary inertial reference point, dynamical analysis indicates that 18 first-
order differential equations of motion are required to mathematically model the system
comprised of the three bodies. This number, however, is reduced by considering the
relative motion. An infinitesimally small point mass, P3 (of mass m3), moving with
respect to two point masses, or primaries, P1 (of mass m1) and P2 (of mass m2), appears in
Figure 2.1. The masses are defined such that 1 2 3m m m> ≫ , restricting the problem in
the sense that all gravitational influence exerted by m3 is neglected. With this
assumption, the motion of P1 and P2 is entirely Keplerian, and reduced to the solution of
the two-body problem. Additionally, this two-body motion is constrained by assuming
that the primaries move in a circular orbit about their common center of mass, or
barycenter, B. As a result, the problem only requires 6 first-order differential equations.
2.1.2 Geometry
An inertial reference frame, I, described in terms of unit vectorsˆ ˆ ˆX Y Z− − , is centered
at B such that the ˆX Y− plane is defined to be coincident with the orbital plane of the
primaries. Since the primary motion is Keplerian, it is constrained to the ˆX Y− plane,
however, the third body can move in any of the three spatial dimensions. A rotating
frame, S, with coordinate axes ˆ ˆx y z− − is initially aligned with I, then rotates through
the angle θ , such that the x -axis is always directed from P1 toward P2. Both the z-
direction and Z -direction are parallel to the orbital angular velocity vector of the
primaries, and, thus, they and Y axes complete the respective right-handed systems.
Due to the circular primary motion, the angular rate, θɺ , is constant and equal to the mean
motion, n. The position of each body, Pi, with respect to the barycenter is defined by iR
,
and the relative position of P3 with respect to P1 and P2 is defined by 13R
and 23R
,
respectively. Note that the overbars (
) indicate vectors.
12
Figure 2.1 Geometry in the Restricted Three-Body Problem
2.1.3 Equations of Motion
One goal of this analysis into the restricted problem of three bodies is a description of
the motion of the infinitesimal mass, P3, subject to the gravitational influence of the
primaries. From Newton’s Second Law, the vector differential equation for motion of P3
is written
21 3 2 3
13 233 2 3 313 23
I d R Gm m Gm mF m R R
dt R R= = − −∑
, (2.1)
where the superscript I represents differentiation in the inertial frame.
A standard nondimensionalization used in the CR3BP is employed here. Since the
mass of the third body is negligible, the characteristic mass, m*, is the sum of the two
primary masses, i.e.,
1 2m m m∗ = + . (2.2)
The characteristic length, l*, is then the constant distance between the primaries, i.e., the
scalar distance,
1 2l R R∗ = + . (2.3)
θ( )2 2 P m
( )1 1 P m
23R
13R
2R
1R
3R
( )3 3 P m
X
Y
x
y
B
13
Finally, the characteristic time, τ*, is defined such that the nondimensonal gravitational
constant, G, is unity, i.e., 1G = . This property is accomplished through the use of
Kepler’s third law, i.e.,
*3
*
l
Gmτ ∗ =
ɶ, (2.4)
where Gɶ represents the dimensional value of the gravitational constant for clarity. These
newly defined natural units lead to the following nondimensional quantities,
2* * * *
, , , ijii ij
RR mr r t
l l m
τµτ
= = = =
, (2.5)
where µ is denoted as the mass ratio. The motion of the third mass is now expressed in
terms of these quantities by dividing equation (2.1) by m3 and the appropriate
characteristic units in equations (2.2)-(2.4),
23
13 232 3 313 23
1I d rr r
dt r r
µ µ−= − −
. (2.6)
The kinematical expansion of the (inertial) first and second derivatives on the left side of
the expression in equation (2.6) exploits the well-known operator relationship,
3 33
I SI I Sdr drr r
dt dtω= = + ×
ɺ , (2.7)
2 23 3 3
3 32 22
I S SI I S I S I Sd r d r drr r
dt dt dtω ω ω= = + × + × ×
ɺɺ , (2.8)
where I Sω is the angular velocity of the rotating frame, S, with respect to the inertial
frame. The second derivative, 2
32
Sd r
dt
, in equation (2.8) can also be expanded
kinematically in terms of the nondimensional, cartesian rotating frame, S,
ˆ ˆ ˆr xx yy zz= + +, (2.9)
3 ˆ ˆ ˆSdr
xx yy zzdt
= + +ɺ ɺ ɺ , (2.10)
3 ˆ ˆ ˆS Sd dr
xx yy zzdt dt
= + +
ɺɺ ɺɺ ɺɺ , (2.11)
14
where dots indicate derivatives with respect to nondimensional time. In this case,
ˆ ˆI S nz zω = =, since the nondimensional mean motion is equal to one. The inertial
acceleration in the rotating frame is expressed by substituting equation (2.11) into
equation (2.8), resulting in the kinematical expansion,
( ) ( )3 ˆ ˆ ˆ2 2I r x y x x y x y y zz= − − + + − +ɺɺ ɺɺ ɺ ɺɺ ɺ ɺɺ . (2.12)
The radius vectors of relative position can also be expanded in terms of the rotating
coordinate frame, i.e.,
( )13 ˆ ˆ ˆr x x yy zzµ= − + +, (2.13)
( )( )23 ˆ ˆ ˆ1r x x yy zzµ= − − + +. (2.14)
Finally, the equations of motion in the rotating frame are derived by combining the
kinematics (equations (2.12)-(2.14)) and the kinetics (equation (2.6)) associated with m3
to yield the following scalar, second-order differential equations,
( )( ) ( )3 3
13 23
1 12
x xx y x
r r
µ µ µ µ− − + −− = − −ɺɺ ɺ , (2.15)
( )3 3
13 23
12
y yy x y
r r
µ µ−+ = − −ɺɺ ɺ , (2.16)
( )3 3
13 23
1 z zz
r r
µ µ−= − −ɺɺ . (2.17)
Equations (2.15)-(2.17) are also written more compactly by introducing the pseudo-
potential function, U,
( ) ( )2 2
13 23
1 1
2U x y
r r
µ µ−= + + + , (2.18)
reducing equations (2.15)-(2.17), i.e.,
2 xx y U− =ɺɺ ɺ , (2.19)
2 yy x U+ =ɺɺ ɺ , (2.20)
zz U=ɺɺ . (2.21)
15
where jj
UU
x
∂=∂
. Equations (2.15)-(2.17) are particularly useful in numerical methods
due to the inherent nondimensional scaling.
2.1.4 Libration Points
Since the equations of motion in the restricted problem do not possess time explicitly
due to the formulation in a rotating frame, the possibility exists for time invariant
equilibrium locations. Such solutions are characterized by stationary position and
velocity in the synodic frame corresponding to the nonlinear system of differential
equations. These particular solutions are determined by nulling the velocity and
acceleration terms in equations (2.15)-(2.17), resulting in the scalar equations,
( )( ) ( )3 3
13 23
1 1
eq eq
eq eq
eq
x xx
r r
µ µ µ µ− − + −− = − − , (2.22)
( )3 3
13 23
1
eq eq
eq eqeq
y yy
r r
µ µ−− = − − , (2.23)
( )3 3
13 23
10 eq eqz z
r r
µ µ−= − − . (2.24)
Equation (2.24) is readily solvable, that is, 0eqz = . Substitution of this result into
equations (2.22) and (2.23) produces a coupled system of two equations and two
unknowns, eqx and eqy . As discovered by Lagrange [9], if 13 23 1r r= = , then equations
(2.22) and (2.23) reduce to identity, implying that two of the equilibrium points are
located at vertices of two unique equilateral triangles. Thus, in cartesian coordinates, the
primaries comprise two of the common vertices of both triangles, with the remaining
vertex defined by 1
2eqx µ= − and 3
2eqy = ± .
16
Three other equilibrium points also exist along the x-axis. Discovered first by Euler
[5], they are denoted the collinear points and can be computed by forcing 0eq eqy z= = .
Substitution into equation (2.22) yields,
( ) ( ) ( )3 3
1 10
1
eq eq
eq
eq eq
x xx
x x
µ µ µ µ
µ µ
− − + −− − =
− + −. (2.25)
Equation (2.25) is a quintic equation in xeq. These solutions require numerical root-
solving methods that ultimately yield three real solutions, labeled L1, L2, and L3. The L1
and L2 points are defined such that L1 is between the primaries, L2 is on the far side of the
smaller mass, and L3 is nearly a unit distance from the larger primary. All five libration
points appear in Figure 2.2.
Figure 2.2 Equilibrium Point Locations for the CR3BP
x
y
B
30
30
6060 1L
2L3L
4L
5L
17
2.1.5 Formulation Relative to P2 in the CR3BP
Using the same rotating frame and characteristic quantities as in the original
development of the equations of motion, an alternate formulation in the CR3BP is
employed when low-thrust terms are included; this alternative formulation can reduce
numerical sensitivity when an additional force of very low magnitude is added to the
model. The origin is shifted from the barycenter to the smaller primary, P2 (as defined in
Figure 2.3), and the equations of motion are rewritten as a function of position, r
, and
velocity, v
, relative to the rotating frame,
( ) ( )r g r h v= + ɺɺ , (2.26)
where
( )( ) ( )
( )3
1 1
1
x x
g r y
r
µ κ µ ρκ
κ
+ − + + + = +
, (2.27)
( )2
2
0
y
h v x
= −
ɺ
ɺ . (2.28)
Note that the kinematical terms in ( )h v
have been shifted to the right side of the
equation. The intermediate term, κ , simply allows equation (2.27) to be written in a
more compact form; it is defined,
( )3 3
1
r
µ µκρ−
= − − . (2.29)
The unit vectors associated with the P2-centered frame are defined parallel to ˆ ˆ ˆx y z− − ,
and are defined as 1 2 3ˆ ˆ ˆr r r− − . The radius vectors of relative position are expanded as
follows,
1 1 2 2 3 3ˆ ˆ ˆr r r r r r r= + +, (2.30)
( )1 1 2 2 3 3ˆ ˆ ˆ1r r r r r rρ = − + +. (2.31)
This formulation is preferred when attempting to accurately propagate finite-thrust
transfers to libration point orbits at L1 and L2.
18
Figure 2.3 Geometry of P2-Centered Rotating Frame
2.2 Natural Periodic Orbits in the CR3BP
In his 1892 study of the n-body problem, “Méthodes Nouvelles de la Méchanique
Celeste” [7], Poincaré focused on the behavior of the nonintegrable solutions as t → ∞ .
There is no practical method of numerical integration to evaluate absolute behavior in
this problem in terms of these conditions. Thus, Poincaré’s investigation focused on the
behavior of periodic orbits – the only viable subset of solutions in the problem of three
bodies for which motion can be predicted as t → ∞ . For nonintegrable dynamical
systems, complete information on an orbit requires either an asymptotic, periodic, or
almost periodic structure. In the CR3BP, the Hamiltonian consistent with a formulation
relative to the rotating frame is time invariant, and an infinite number of periodic
solutions are available. Initially, insight concerning the different types of orbital motion
in the restricted problem is gained by linearizing relative to the libration points. A first-
order linearization process yields approximate gradient information for use in evaluating
the stability of the equilibrium points, as well as applications to some nonlinear targeting
algorithms. Also, the linear solutions are eventually extended into the actual nonlinear
problem by numerically solving a two-point boundary-value problem that exploits
symmetry across the x-axis.
1r
2r
r
( )1 1 P m ( )2 2 P m
( )3 3 P m
ρ
*l
19
2.2.1 First-Order Variational Equations Relative to the Collinear Points
Analyzing the linear equations in the neighborhood of a libration point offers
information concerning potential bounded behavior. Let the variables, ξ, η, and ζ
indicate the relative position of the third body, or spacecraft, with respect to the collinear
libration point, i.e.,
eqx xξ = − , eqy yη = − , eqz zζ = − . (2.32)
Linearizing the equations of motion in equations (2.15)-(2.17) relative to the libration
point and using a first-order Taylor series expansion, results in an expression of the
following form,
ξ ξ ξη η ηζ ζ ζ
= +
B C
ɺɺ ɺ
ɺɺ ɺ
ɺɺ ɺ, (2.33)
where the matrices B and C are defined as follows,
eq
xx xy xz
yx yy yz
zx zy zz X X
U U U
U U U
U U U=
=
B
, (2.34)
0 2 0
2 0 0
0 0 0
= −
C . (2.35)
The subscripts “ij ”, on ijU denote evaluation of the second partial derivative of the
pseudopotential function, 2U
j i
∂∂ ∂
. Note that the B matrix is evaluated on the reference, in
this case, the libration point, eqX X=
. Thus, the differential equation in equation (2.33)
is linear with constant coefficients. The relationship is rewritten in state-space form,
γ γ= A ɺ , (2.36)
where
Tγ ξ η ζ ξ η ζ = ɺ ɺ ɺɺ , (2.37)
20
3 =
0 IA
B C. (2.38)
The A matrix is a 6x6 matrix composed of four 3x3 submatrices, the matrix 0 is simply a
matrix of zeros, and I 3 is defined as the 3x3 identity matrix. Evaluating the C-matrix at
the collinear libration points, where yeq = zeq = 0, yields
0eq eq eq eq
xz yz zx zyX X X X X X X XU U U U
= = = == = = = , 0
eqzz X X
U=
< , (2.39)
0eq eq
xy yxX X X XU U
= == = , 0
eqxx X X
U=
> , 0eq
yy X XU
=> . (2.40)
Equations (2.38)-(2.40) simplify the linearized equations of motion to the form,
0 0
0 0
0 0eq
xx
yy
zz X X
U
U
U
ξ ξ ξη η ηζ ζ ζ
=
=
+ C
ɺɺ ɺ
ɺɺ ɺ
ɺɺ ɺ. (2.41)
From inspection of the third row of equation (2.41), it is clear that the out-of-plane
component, ζ , is completely decoupled from the independent variables, ξ and η . The
characteristic equation for this out-of-plane motion in equation (2.41) also possesses
purely imaginary roots. Thus, the solution is oscillatory,
1 2cos sin C t C tρ ω ω= + , (2.42)
where the frequency is eq
zz X XUω
== . The first two rows in equation (2.41) are coupled
through the matrix C and represent the in-plane motion, with a general solution of the
following form,
4
1
i ti
i
Aeλξ=
=∑ , (2.43)
4
1
i ti
i
B eλη=
=∑ , (2.44)
where Ai and Bi are dependent. To determine the eigenvalues, iλ , Szebehely [10] uses a
special form of the characteristic equation (a quadratic), i.e.,
2 21 22 0β βΛ + Λ − = , (2.45)
where
21
1 22
eq eqxx yyX X X X
U Uβ
= =+
= −
, (2.46)
22 0
eq eqxx yyX X X X
U Uβ= =
= − > , (2.47)
λ = ± Λ . (2.48)
The quadratic roots are determined first, i.e.,
2 21 1 1 2 0β β βΛ = − + + > , (2.49)
2 22 1 1 2 0β β βΛ = − − + < . (2.50)
Substituting equations (2.49)-(2.50) into equation (2.48) reveals the four characteristic
roots,
1,2 1λ = ± Λ (real), (2.51)
3,4 2λ = ± Λ (imaginary). (2.52)
Of course, the positive real root in equation (2.49) results in a positive real eigenvalue in
equation (2.51) and unbounded behavior in the general solution in equations (2.43)-(2.44)
as t → ∞ . The dependency between Ai and Bi is resolved by substituting equations
(2.51)-(2.52) into the first two expressions in equations (2.41) and simultaneously solving
the equations, thus,
2eq
i xx X X
i i i ii
UA A
λβ α
λ=
−= =
. (2.53)
Equation (2.43)-(2.44) and their derivatives are evaluated at the initial time to determine
the unknown initial conditions. Equation (2.53) is substituted into equation (2.44) so that
the results are entirely in terms of the independent variables Ai, i.e.,
0
4
01
i ti
i
Aeλξ=
=∑ , (2.54)
0
4
01
i ti i
i
Aeλξ λ=
=∑ɺ , (2.55)
0
4
01
i ti i
i
Aeλη α=
=∑ , (2.56)
22
0
4
01
iti i i
i
Aeλη α λ=
=∑ɺ . (2.57)
Since the coefficients A1 and A2 are associated with the real eigenvalues in equations
(2.51)-(2.52), fixing the values of A1 and A2 to zero ensures that any exponential increase
and decay is suppressed. Thus, a bounded planar solution emerges,
( ) ( )00 0 0
3
cos sins t t s t tηξ ξβ
= − + −
, (2.58)
( ) ( )0 0 3 0 0cos sins t t s t tη η β ξ= − − − , (2.59)
where
3 isλ = , (2.60)
( )1 22 21 2 3s β β β= + + , (2.61)
2 *
3 2xxs U
sβ += , (2.62)
3 3iα β= . (2.63)
Once the initial conditions 0ξ and 0η are selected, 0ξɺ and 0ηɺ are predetermined to
enforce A1 = A2 = 0. The resulting orbit is an ellipse with a collinear libration point at the
center. The semimajor axis is parallel to the unit vector y and the semiminor axis is in
the x-direction. An example of such an orbit about L1 in the Earth-moon system appears
in Figure 2.4. The period is 2
IPs
π= for the planar motion, and the root, s, may be
selected to achieve a specific value of IP. In this linear model, the out-of-plane
frequency is not commensurate with the in-plane frequency. Nevertheless, these planar
solutions form the basis for determining initial conditions consistent with planar
nonlinear orbits; such planar orbits that are solutions in the nonlinear problem are then
used in computing fully three-dimensional nonlinear orbits. Although the first-order
linear set of initial conditions do not result in a closed periodic orbit in the nonlinear
problem, initial conditions “sufficiently close” to the libration point still yield an initial
guess that may be used in targeting algorithms.
23
Earth-Moon System
[ ]0 0.1 0 0 0 - 0.837227214342055 0Tγ =
0.0121505649407351µ = , 1 unit = 385692.5 km
Figure 2.4 Linearized L1 Periodic Orbit
2.2.2 The State Transition Matrix
Besides stationary equilibrium points, dynamical information is also sought relative to
time-varying solutions in the nonlinear problem. Numerical computation of trajectory
arcs, such as periodic orbits and orbital transfers to a specific target in the CR3BP,
requires the use of differential corrections procedures. Before introducing these
corrections procedures, an important result from linearization of the equations of motion
is required. Define ( )*X t
as a time-varying reference solution such that
( ) ( ) ( )*X t X t X tδ= +
. Let the perturbed state relative to the reference trajectory be
defined as,
Moon To Earth
L1
24
( ) TX t x y z x y zδ δ δ δ δ δ δ=
ɺ ɺ ɺ . (2.64)
Recall that a less general expansion was introduced in equation (2.36) with a constant
reference. Linearizing the nonlinear equations of motion in equations (2.15)-(2.17) with
a first-order Taylor series expansion results in the familiar equation
( )X t Xδ δ= A ɺ , (2.65)
where
( ) ( )3t
t
=
0 IA
B C. (2.66)
Since all linearization now employs a time-variable trajectory as the reference, the A-
matrix is now time-dependent. This time dependency occurs specifically in the partial
derivatives of the B-matrix (see equation (2.34)) where the second partials are evaluated
along the reference trajectory path. The solution of the linear system of equations in
equation (2.65) is expressed in terms of the state transition matrix, ΦΦΦΦ , such that,
( ) ( ) ( )0 0 0 0,X t t t t t X tδ δ+ = +
ΦΦΦΦ , (2.67)
where the matrix ΦΦΦΦ is obtained via the matrix differential equation,
( ) ( ) ( )0 0 0 0, ,t t t t t t t+ = +AɺΦ ΦΦ ΦΦ ΦΦ Φ . (2.68)
Numerical integration of equation (2.68) requires equation (2.66) to be evaluated along
the trajectory at all times, and is, thus, simultaneously integrated along with equations
(2.15)-(2.17). Once obtained, the state transition matrix is essentially a sensitivity matrix
that is an approximate mapping of trajectories in the neighborhood of the reference.
Decomposed into components, the 6x6 matrix is represented as,
25
( )
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 00 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0
,
x x x x x x
x y z x y z
y y y y y y
x y z x y z
z z z z z z
x y z x y zt t t
x x x x x x
x y z x y z
y y y y y y
x y z x y z
z z
x y
∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂
+ =∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂ ∂ ∂ ∂∂ ∂ ∂∂ ∂
ɺ ɺ ɺ
ɺ ɺ ɺ
ɺ ɺ ɺ
ɺ ɺ ɺ ɺ ɺ ɺ
ɺ ɺ ɺ
ɺ ɺ ɺ ɺ ɺ ɺ
ɺ ɺ ɺ
ɺ ɺ ɺ
ΦΦΦΦ
0 0 0 0
z z z z
z x y z
∂ ∂ ∂ ∂ ∂ ∂ ∂
ɺ ɺ ɺ
ɺ ɺ ɺ
. (2.69)
If an exact solution to the equations of motion is available, it is possible that equation
(2.68) can be integrated analytically. Otherwise, it may be necessary to numerically
integrate equation (2.68) to generate a time-varying history for ΦΦΦΦ .
2.2.3 The Fundamental Targeting Relationships
The availability of the STM is a critical component in any targeting algorithm.
Consider the initial state of a spacecraft on a reference path, ( )*0X t
; then, the reference
state at some future time can be denoted as ( )*0X t t+ ∆
. Note that any superscript ‘*’
refers to a condition on the reference path. Such points are represented as points A and C,
respectively, in Figure 2.5. Point C, downstream from point A along the reference path,
can be modeled as a numerical mapping of the initial state and the time interval, t∆ ,
( ) ( )( )* *0 0 ,X t t f X t t+ ∆ = ∆
. (2.70)
The initial state at time 0t on some neighboring trajectory is represented by ( )0X t
. A
contemporaneous variation at point A shifts the spacecraft onto point B along X
at time
0t , such that a new perturbed state is defined ( ) ( )*0 0X t X t Xδ= +
. After a specified
time interval, t∆ , A is mapped to C and B is mapped to D′ . Thus, ( )0X t tδ + ∆
26
represents the six-dimensional state at D′ with respect to C and is the contemporaneous
variation at time *0t t t= + ∆ . Let point D be some other point along the neighboring
path. Point D is achieved via an arbitrary time interval relative to B, i.e., ( 0t t tδ+ ∆ + ).
Thus, comparing the state at D to the point C along the reference path defines a
noncontemporaneous variation, ( )0X t t tδ δ+ ∆ +
. The noncontemporaneous variation in
the state between points C and D is mapped as a function of the reference state and any
additional state and time perturbation,
( ) ( )* *0 0,fX t t X f X X t tδ δ δ+ ∆ + = + ∆ +
. (2.71)
For a general targeting algorithm, consider the governing differential equations from
equations (2.15)-(2.17) as written in first-order form,
( ),X f X t= ɺ . (2.72)
Let *X X Xδ= +
, and *t t tδ= + . The differential equations on the neighboring path B-
D are written as,
( )* *,X X f X X t tδ δ δ+ = + + ɺ ɺ . (2.73)
Given equation (2.73), a first-order Taylor series expansion about the reference path A-C,
yields,
( )* *
* *
* * *,X X X X
t t t t
f fX X f X t X t
X tδ δ δ
= =
= =
∂ ∂+ = + +∂ ∂
ɺ ɺ . (2.74)
Equation (2.74) is reduced by noting that equation (2.72) eliminates *Xɺ and ( )* *,f X t
.
Then, *
*
X X
t t
f
X =
=
∂∂
is simply ( )tA , and *
*
X X
t t
f
t =
=
∂∂
is an additional matrix of partial time
derivatives, denoted as ( )tK , i.e.,
( ) ( )X t X t tδ δ δ= +A K ɺ . (2.75)
27
The solution to equation (2.75) is,
( ) ( ) *
*
0 0 0 0,X X
t t
X t t t t t t X X tδ δ δ δ=
=
+ ∆ + = Φ + ∆ + ɺ . (2.76)
where the following definitions, apparent in Figure 2.5, apply, such that,
( ) ( )*0 0 fX t t t X t t Xδ δ+ ∆ + = + ∆ +
, (2.77)
( )0 0X X tδ δ=
, (2.78)
( )0fX X t t tδ δ δ= + ∆ +
. (2.79)
Figure 2.5 Basic Diagram for a Free Final Time Targeting Scheme
In a more compact form, equation (2.76) becomes,
( ) ( )* 00 0 0,f
XX t t t X t t
t
δδδ
= + ∆ + ∆
ɺΦΦΦΦ , (2.80)
where it is emphasized that ( )0 0,t t t+ ∆ΦΦΦΦ and ( )*0X t t+ ∆
ɺ are evaluated on the reference
path. Equation (2.80) is the fundamental basis of most numerical targeting schemes. For
time-fixed problems, equation (2.80) simplifies to
X
A
B
C
D′D
t
*X
tδ
( )0X t tδ + ∆
( )0 fX t t Xδ δτ δ+∆ + = ( )0X tδ
t∆0t
X
Reference Path
Neighboring Path
Initial State
Target
28
( )0 0 0,fX t t t Xδ δ= + ∆
ΦΦΦΦ . (2.81)
Thus, equations (2.80)-(2.81) comprise a process for approximating state sensitivities,
given initial variations in the state vector. Note that the columns of ( )0 0,t t t+ ∆ΦΦΦΦ are
associated with the control parameters, 0Xδ
and tδ , and the rows correspond to the end-
point constraint parameters, fXδ
.
2.2.4 Periodic Orbits
Simple targeting procedures to converge upon a periodic orbit often exploit symmetric
behavior; additional conditions at various plane crossings can also benefit the process.
Consider the planar problem of computing periodic Lyapunov orbits in the vicinity of L1
in the Earth-moon system. Due to the x-axis symmetry, only the first half-period of the
orbit must be determined. Thus, for some given initial state on the x-axis, represented by
0X
in Figure 2.6, an arbitrary initial velocity, 0yɺ , will produce the dashed curve in
Figure 2.6. This initial state is specified by nonzero state components in only the x-
direction and the yɺ -direction in terms of the linearized system,
0 0 00 0T
X x y=
ɺ . (2.82)
Correction of the nonlinear propagation results in a perpendicular crossing (the solid
curve in Figure 2.6), but requires the solution of a two-point boundary-value problem.
Satisfying the constraint is accomplished by adjusting one of the two nonzero initial
states, as well as the propagation time. For purposes of demonstration, variations on the
initial y-velocity, 0yɺ , are permitted, i.e., 0yδ ɺ and tδ are controls, while all other initial
variations remain fixed, i.e.,
0 0xδ = , 0 0yδ = , 0 0xδ =ɺ . (2.83)
29
When the trajectory terminates after a half-period, the final state will be of the following
form,
0 0T
f f fX x y=
ɺ . (2.84)
Note that determination of the perpendicular x-axis crossing requires two constraints:
one to enforce termination of the trajectory on the x-axis; and, one to enforce elimination
of the final x-velocity, i.e.,
0fyδ = , (2.85)
0fxδ =ɺ . (2.86)
The control on the time variation, tδ , is only implicit since the x-axis crossing is always
forced, and thus, equation (2.81) is always satisfied. The constraint in equation (2.86)
remains active until the actual final x-velocity, af
xɺ , is equal to the desired final x-
velocity, df
xɺ , i.e., zero. Therefore,
d a af f f fx x x xδ = − = −ɺ ɺ ɺ ɺ . (2.87)
The variational relationship between the initial and final states for a perpendicular
crossing is the result of inserting equations (2.84)-(2.85) and equation (2.87) into
equation (2.80),
0 0
0
0
T
f
yy
y y
x x tx
y
δδ
∂ ∂ = − ∂ ∂
ɺɺ ɺ
ɺ ɺɺɺ
ɺ
. (2.88)
Equation (2.88) is solved for the initial variation in the y-velocity,
00 0
af
x x yy x
y y yδ
∂ ∂= − − ∂ ∂
ɺ ɺɺɺ ɺ
ɺ ɺ ɺ. (2.89)
Equation (2.89) produces the estimate for the control necessary to meet the constraints.
Since this update is based upon a linear approximation, an iterative process is employed.
Thus, the initial y-velocity is updated until equation (2.86) is satisfied numerically, that
is, until df
x ε<ɺ , where ε is a user-defined error tolerance.
30
The computation of an entire family of periodic orbits requires a method of
continuation to produce the new initial conditions. Given one periodic orbit, a second
orbit is sought by generating a guess for initial conditions that are suitable to yield a
second periodic orbit. To produce the new guess, a new state is generated by
incrementing the value of x0 and using the previously successful value of 0yɺ as an initial
guess to initiate the new corrections process. An example result of such a process
appears in Figure 2.7.
Figure 2.6 Targeting a Perpendicular x -axis Crossing in the CR3BP
x
y
0X
dfX
afX
1L
31
Earth-Moon System
0.0121505649407351µ = , 1 unit = 385692.5 km
Figure 2.7 Several L1 Lyapunov Orbits Obtained Via Continuation
2.3 Invariant Manifolds
Henri Poincaré [7] determined that any dynamical system can be analyzed from a
geometrical perspective. Knowledge of the phase-space of a dynamical system allows
the decomposition of the flow into subspaces, thus characterizing the behavior of a
system. In obtaining transfer paths in the CR3BP, it is very useful to exploit the
knowledge of the flow in the vicinity of a reference solution. Not only does this
dynamical systems analysis allow insight into the motion in the vicinity of the libration
points, it is particularly significant for an understanding of the behavior near the periodic
orbits.
Moon
L1
To Earth
32
2.3.1 Stable and Unstable Manifolds Associated with the Collinear Points
Analysis of dynamical systems from the perspective of manifolds is available in
various references [47-51], but a summary is useful. Consider the first-order form of the
equations of motion (equation (2.72)) that may be employed to equivalently represent a
general nonlinear vector field. For such a general field, let the state vector ( ) nX t ∈
ℝ ,
and : nf Ψ →
ℝ be a smooth function over the subset nΨ → ℝ generating a flow,
( )TΓ Ψ . The flow depends upon the initial condition 0X
, so it is also equivalently
written as ( )0T tΓ , and may be propagated over time t. In the dynamical system of
interest, one important aspect of the behavior of the flow can be evaluated via a linear
stability analysis. To introduce this topic, use a time-invariant system as an example. In
the Earth-moon system, the equilibrium point at Li is a solution to the nonlinear equations
(2.15)-(2.17); these equations, of course, can be written in the form in equation (2.72).
Recall that a Taylor series expansion results in the vector linear variational equation,
equation (2.36), with constant Jacobian matrix, A. As in section 2.2.1, the reference
solution is the fixed equilibrium point at Li, that is, the constant ref eqX X=
. The solution
to equation (2.36) appears in the following form [49],
( ) ( ) ( )0
0t tt e tγ γ−= A
. (2.90)
The eigenvalues, jλ , of the A-matrix are distinct [49] and thus, linearly independent.
Equation (2.84) is further simplified as follows
( ) ( ) ( )0 -10
t tt e tγ γ−= N N ΛΛΛΛ , (2.91)
where the matrix of eigenvectors, 1 2 ... nN N N = N
, corresponds to the diagonal
matrix of eigenvalues, ΛΛΛΛ , containing entries 1 2, ,..., nλ λ λ . Thus, every jN
corresponds to
jλ in equation (2.91). The matrix ( )0t te −ΛΛΛΛ is diagonal or block diagonal, and the
eigenvalues, jλ , are denoted as the characteristic exponents associated with the local
flow. The linear system is decomposed into a sum that is a function of each mode,
33
( ) ( )0
1
j
nt t
j jj
t c e Nλγ −
=
=∑
, (2.92)
where the coefficients cj are determined from ( )0tγ .
The matrix A is composed of a total of n eigenvalues. Of this total set, nu eigenvalues
are positive real or possess positive real components, ns are defined with negative real
components, and nc eigenvalues are purely imaginary. Thus, n = nu + ns + nc. Due to the
linear independence of the system, the solution space is comprised of the corresponding
invariant subspaces uE , sE , and cE . Flow that is contained within one subspace at the
initial time will remain in that subspace throughout the dynamical evolution of the
system. Trajectories within the uE subspace approach 0γ =
as t → −∞ , those within
sE subspace approach 0γ =
as t → +∞ , and those within cE neither grow or decay over
time. Note that when all of the eigenvalues of A possess non-zero real parts, eqX
is
defined as a hyperbolic equilibrium point. These observations allow for the introduction
of the Stable Manifold Theorem for Flows [49].
Theorem 2.1: (Stable Manifold Theorem for Flows). Suppose that ( )X f X=
ɺ has a
hyperbolic equilibrium point eqX
. Then there exist local stable and unstable manifolds
( )locs eqW X
, ( )locu eqW X
, of the same dimensions ns, nu as those of the eigenspaces sE and
uE of the linearized system, and tangent to sE and uE at eqX
. ( )locs eqW X
and
( )locu eqW X
are as smooth as the function f
.
A center subspace, cE , associated with the linear system and corresponding to nc also
exists, however, ( )locc eqW X
is not necessarily tangent to cE [49]. As an example of
Theorem 2.1, consider a constant equilibrium point. A conceptual representation of the
Stable Manifold Theorem in a two-dimensional phase space appears in
34
Figure 2.8. Note the flow toward and away from the equilibrium point. The eigenvectors
sV
and uV
correspond to the stable and unstable eigenvalues, respectively, and span the
subspacessE and uE to form a vector basis in 2ℝ . For the nonlinear system, sV
and uV
can be used to numerically approximate the manifolds in the nonlinear system
(locsW ,
locuW ) near eqX
. The local stable manifold, ( )locs eqW X
, includes every initial
condition that produces trajectories that asymptotically approach the equilibrium point,
converging along the one-dimensional eigenvector tangent to sE , in both the positive and
negative directions. Conversely, the local unstable manifold, ( )locu eqW X
, contains every
initial condition that produces a trajectory that asymptotically departs the equilibrium
point. Near eqX
, it is tangent to uE in the positive and negative directions along the
corresponding one-dimensional eigenvector. The local manifolds, locsW and
locuW , also
possess global analogs sW and uW . Actual computation of these global manifolds
requires numerical simulation forward and backward in time using initial conditions from
sE and uE . Thus, the global flow is extended as a function of the localized behavior,
( ) ( )0
locs ref T s reft
W X W X≤
= Γ
∪ , (2.93)
( ) ( )0
locu ref T u reft
W X W X≤
= Γ
∪ . (2.94)
Note that these definitions can be extended to a generalized reference, refX
.
Figure 2.8 Stable and Unstable Manifold at eqX
eqX
locuW −
locsW −
uV
sV
uV−
sV−sE
uE
locsW +
locuW +
35
2.3.2 Invariant Manifolds Relative to a Fixed Point
In addition to individual equilibrium points that serve as a reference solution,
dynamical systems theory also supports periodic orbits as time-varying reference
solutions. Consider the monodromy matrix, i.e., the state transition matrix at the end of
one period, ( ),0IPΦΦΦΦ . The state transition matrix is a linear map, thus, the monodromy
matrix defines a linear, stroboscopic map sampled over one period. Essentially, by
creating a map, the continuous time system is transformed to a discrete time system. Any
point along a periodic orbit can be used to create a map; this point is termed a “fixed
point”. For planar periodic orbits in the vicinity of the collinear libration points, one of
the two points on the x-axis is initially convenient as a fixed point, but not necessary.
After one revolution at this discrete reference point, the eigenvalues and eigenvectors of
the monodromy matrix characterize the local phase space similar to the subspaces in the
analysis of an equilibrium point. However, in the case of a periodic orbit, the fixed point
is redefined and the monodromy matrix must be recomputed at different points of interest
along the orbit, as the behavior associated with the local phase space varies. This varying
behavior along the orbit, characterized by different discrete points, results in unique
eigenvectors. The corresponding eigenvalues, however, do not change since they are a
property of the orbit [49].
For periodic orbits, recall that the linearized equations of motion (equation (2.65))
result in a time varying matrix A(t) to be evaluated at all points along the orbit. As a
consequence of Floquet Theory [49], the STM can be rewritten in the general form,
( ) ( ) ( )-1,0 0tt t e= JF FΦΦΦΦ , (2.95)
where ( )tF is a periodic matrix, and J is a normal, block diagonal matrix whose
elements are termed the Poincaré exponents. Since ( )tF is a periodic matrix,
( ) ( )0IP =F F . Thus, the monodromy matrix can be expressed,
( ) ( ) ( )-1,0 0 0IPIP e= JF FΦΦΦΦ . (2.96)
Rearranging equation (2.96),
36
( ) ( ) ( )-1 0 ,0 0IPe IP=J F FΦΦΦΦ . (2.97)
Equation (2.97) is an explicit statement that ( )0F is the eigenvector matrix associated
with the monodromy matrix. The corresponding eigenvalues are contained in the matrix
IPeJ . Defining the eigenvalues of ( ),0IPΦΦΦΦ as iλ ; they are labeled the characteristic
multipliers. Then, iρ , the diagonal entries of J, are defined such that,
i IPi eρλ = , (2.98)
or equivalently,
1lni ie
IPρ λ= . (2.99)
The Poincaré exponents, iρ , in equation (2.99) occur in positive/negative pairs according
to Lyapunov’s theorem [51]. The stability associated with the fixed point of interest
along the periodic orbit can now be summarized as follows. Assuming a hyperbolic
system,
if 1iλ < for all iλ , 0X =
as t → ∞ asymptotically stable,
if 1iλ > for all iλ , 0X =
as t → −∞ unstable.
The stability of any fixed point, i.e., the behavior of iλ , represents the stability of the
periodic orbit. Moreover, due to the nature of the Poincaré exponents, along with an
inspection of equation (2.98), it is apparent that the eigenvalues associated with a fixed
point along the periodic orbit occur in complex conjugate pairs. Two of the eigenvalues
will equal one, indicating a periodic orbit and, therefore, are subsequently used to reduce
the dimension on the system and create the map. In Lyapunov orbits (in addition to all
three-dimensional orbits considered later), the remaining real pair is used to specify the
stable/unstable subspaces.
37
2.3.3 Computation of Manifolds Corresponding to Fixed Points Along an Orbit
Globalizing the stable and unstable manifold trajectories that correspond to fixed
points along a periodic orbit requires initial conditions in the subspaces sE and uE .
These conditions are estimated given the state and phase space information
corresponding to any fixed point, ( )fp iX t
along the periodic orbit. Of course, any state
along the orbit, ( )fp iX t
, will remain on the periodic orbit when propagated. To globalize
the manifold trajectory and compute the flow toward and away from the periodic orbit,
conditions on the manifold must be approximated near the fixed point. Thus, given
( )fp iX t
, a perturbation is added to shift the state into the desired subspace. Computation
of this new state is accomplished by defining a perturbation in the direction of the stable
or unstable eigenvector by some small distance d. If the eigenvectors associated with the
stable and unstable mode are defined as ( ) [ ]ˆ sTW
i s s s s s sY t x y z x y z= ɺ ɺ ɺ and
( ) [ ]ˆ uTW
i u u u u u uY t x y z x y z= ɺ ɺ ɺ respectively, then a normalization in position
results in the definitions,
( ) ( )2 2 2
ˆ s
s
WW i
i
s s s
Y tV t
x y z=
+ +
, (2.100)
( ) ( )2 2 2
ˆ u
u
WW i
i
u u u
Y tV t
x y z=
+ +
. (2.101)
The initial state vector to shift intosE or uE is then represented by the full expressions
( ) ( ) ( )sWs i fp i iX t X t d V t= ± ⋅
, (2.102)
( ) ( ) ( )uWu i fp i iX t X t d V t= ± ⋅
. (2.103)
The alternating signs on the displacement from ( )fp iX t
in equations (2.102)-(2.103)
represent the fact that the trajectory may be perturbed in either direction in the stable or
unstable subspace, as depicted in Figure 2.8. Propagating the states in equation (2.102) in
positive time at fixed states along the entire orbit results in globalization of the stable
manifold. Repeating the process on the states in equation (2.103) in negative time results
38
in globalization of the unstable manifold. Typically, in the Earth-moon system, a value
of d = 50 km is sufficient to justify the linear approximation, yet still yield adequate
integration time. An example Lyapunov orbit near L1 in the Earth-moon system with y-
amplitude Ay = 23,700 km appears in Figure 2.9. At various fixed points around the
orbit, the eigenvectors of the monodromy matrix are computed and the initial states are
shifted according to equations (2.102)-(2.103). The initial conditions are then
numerically integrated in both directions. For the example in Figure 2.9, the number of
fixed points examined is specified as nfp; for this case nfp = 50. The initial states are
specified such that ( ) ( )fp i fp fpX t X iIP n=
, where IP is the orbital period and i =
1,2,3,…,nfp.
Earth-Moon System
0.0121505649407351µ =
Figure 2.9 Global Manifolds for an Earth-Moon L1 Lyapunov Orbit, Ay = 23,700 km
Earth
Moon
Unstable
Stable
Unstable
Stable
L1
39
1,2,3,…,nfp. For each fixed point, the four sets of initial conditions on ( )s iX t
, ( )u iX t
are then numerically integrated in both directions until the Earth-relative flight path angle
is zero for Earth-bound trajectories, or until the moon-relative flight path angle is zero for
moon-bound trajectories. Thus, the red families of trajectories in Figure 2.9 represent the
trajectories that asymptotically diverge from the orbit. Such trajectories are useful for
transfer trajectories that begin at the orbit, and depart to another region of interest (for
example, the surface of the moon). The green families of trajectories in Figure 2.9
represent the trajectories that asymptotically approach the orbit, and are exploited for
purposes of transfers from a region of interest (for example, an Earth parking orbit) for
delivery of a spacecraft into a libration point orbit.
2.4 Optimal Control Theory
Computation of manifold trajectories that depart from or arrive at libration point orbits
comprises only part of the necessary background to develop a finite-thrust transfer. In
the Earth-moon problem, for example, the manifolds do not pass very close to the Earth
(see Figure 2.10). One design option to gain access to a manifold from the Earth, is a
low-thrust transfer arc. Defining such an arc requires the determination of a thrust
magnitude and directional history. Optimal control theory can be employed to solve this
“steering” problem.
The term “optimal control” is used to reference a branch of mathematics formally
known as the calculus of variations, although the former term is commonly employed in
dynamical applications [25]. The solution to a typical optimal control problem requires
the determination of a set of conditions that minimize or maximize a scalar performance
index with respect to admissible comparison paths, subject to differential constraints and
boundary conditions. To determine the solutions, i.e., “extremals”, a Taylor series
expansion leads to necessary and sufficient conditions for a local minimum. As
expected, the first necessary condition requires that the differential of the performance
index vanishes, and the second necessary condition requires non-negativity on the second
40
differential of the performance index. A stronger, and more general second necessary
condition for a minimum is Pontryagin’s Minimum Principle. Note that the second
necessary condition can also be viewed as a test to determine if the extremal (determined
from the first necessary conditions) is actually a minimum or a maximum. Ultimately,
for a solution to the optimal control problem, the determination of these extremals
requires the solution of a two-point boundary-value problem (TPBVP) that may or may
not be solvable in closed form. Because optimal control theory relies on comparing
different neighboring paths, it is useful to note that a ‘**’ superscript indicates a
condition on the optimal path.
2.4.1 Summary of the First Necessary Conditions for Optimal Control
Consider the free final time problem for the control history, ( )u t
, that minimizes the
generalized scalar performance index, J, or cost function at a final time,
( ) ( )0
, , , ,ft
Tf f
t
J X t H X u t X dtφ χ χ = + − ∫
ɺ , (2.104)
subject to the n differential constraints,
( ), ,n nX f X u t= ɺ , (2.105)
the n+1 prescribed initial constraints,
( )1 0 0, 0n X tϕ + =
, (2.106)
and the p prescribed final constraints
( ) 0p fXψ =
, (2.107)
where p is some integer value such that 0p n≤ ≤ . Equation (2.107) is denoted the set of
kinematic boundary conditions, and is a function of the final state. In equation (2.104),
( ),f fX tφ
is an endpoint function, and the second term is a path-wise function. The
scalar H is the Hamiltonian defined as,
41
( ) ( ) ( ), , , , , , ,nH X u t L X u t f X u tχ χ Τ= +
, (2.108)
where ( ), ,L X u t
is a path-wise component to be minimized, and the second term is a set
of Lagrange multipliers or “costates”, ( ), ,X u tχ
, adjoined to the differential constraints.
A minimization problem that only contains the endpoint function, ( ),f fX tφ
, is
commonly known as a Mayer problem. Those that only include path-wise components
within the integral are Lagrange Problems. Finally, Bolza problems are defined as those
that include both components. For the general Bolza problem stated in equation (2.104),
the following assumptions are also imposed:
1. Define Ω as an arbitrary set in m-dimensional Euclidean space; the “measurable”
control ( )u t
, defined over the closed time interval 0, ft t , possesses values lying
within Ω , i.e., ( )u t ∈Ω. For all applications considered here, ( )u t
is a
bounded, piecewise continuous function.
2. The functions ( ), ,nf X u t
, ( ), ,nf X u t
X
∂∂
,( ), ,nf X u t
t
∂∂
, ( ), ,L X u t
,( ), ,L X u t
X
∂∂
, and
( ), ,L X u t
t
∂∂
are all continuous functions of ( ), ,X u t
. Since ( ), ,nf X u t
u
∂∂
and
( ), ,L X u t
u
∂∂
do not need to exist, nf
or L may contain instantaneous switching
conditions on the control, i.e., u
-type terms.
3. The endpoint component of the Bolza function, ( ) 1,f fX t Cφ ∈
, and the final
boundary conditions at ( ) 1, f fX t Cψ ∈
, are continuously differentiable in all
arguments; the vectors i
fX
ψ∂∂
for 1,...,i p= are linearly independent in the region
of the optimal candidates ( )0 0, , ,f fX t X t
.
42
Additional sets of Lagrange multipliers, ϖ and υ , are adjoined to the initial and final
endpoint constraints, ( )0 0,X tϕ
, and ( )fXψ
, to construct the augmented performance
index,
( )( )0
, , , ft
T
t
J H X u t X dtχ χ′ = Θ + −∫ ɺ , (2.109)
where
( ) ( ) ( )0 0, ,T Tf f fX t X t Xφ ϖ ϕ υ ψΘ = + +
. (2.110)
Minimizing the performance index (equation (2.104)) is equivalent to minimizing the
augmented performance index (equation (2.109)) if all constraints are satisfied. Of
course, the minimization process initially requires that the first differential of the
performance index, dJ′ , vanishes. As a result, several necessary conditions arise. First,
the well-known Euler-Lagrange equations must be satisfied to obtain an optimal state,
( )**X t
, appropriate costates, ( )tχ and optimal control history, ( )**u t
[25],
( ), , ,TxH X u tχ χ= −
ɺ (n-equations), (2.111)
( )0 , , ,uH X u tχ=
(m-equations). (2.112)
Because of the dependence on the states, the costate equations of motion (equations
(2.111)) are integrated simultaneously with the state equations (equations (2.105)). Note
that equations (2.105), (2.111), and (2.112) comprise the (2n + m) equations that are
necessary to determine the n values of the state, ( )X t
, n costates ( )tχ , and m controls
( )u t
. To specify a complete two-point boundary-value problem (TPBVP), a total of (2n
+ 2) boundary conditions are required corresponding to the 2n initial and final states, as
well as the initial and final times. From equations (2.106)-(2.107), (p + n + 1) boundary
conditions are already available, leaving (n + 1 – p) boundary conditions that remain to
be specified. These remaining boundary conditions are determined from the
transversality condition (the final condition needed for 0dJ = ), or from the natural
boundary conditions (the final conditions needed for 0dJ′ = ). This development relies
on the augmented performance index, however, further detail on the transversality
43
condition is available in Bryson and Ho [24]. Given the expression for the differential of
the augmented performance index, the additional (n + 1 – p) boundary condition
equations, that is, the natural boundary conditions arise,
00 , , ff
T Tf f tX X
Hχ χ= −Θ = Θ = −Θ
. (2.113)
When equations (2.113) are satisfied, the boundary conditions correspond to an identical
extremal value of the performance index computed via the transversality conditions.
Equations (2.111)-(2.113) are derived by equating the first differential of the augmented
performance index to zero, and are, thus, only necessary conditions for a minimum value
of J. But, for unbounded controls, equation (2.112) typically yields the control equation.
For bounded controls, other approaches are often employed. Of course, when the
performance index is to be maximized, J can simply be replaced by –J, and the same
minimization process proceeds.
2.4.2 Tests for a Local Minimum Value of the Performance Index
The Euler-Lagrange equations result from the determination of stationary conditions
on the first differential of the augmented performance index. Thus, the Euler-Lagrange
equations are valid for either a maximum or minimum value. To ensure that the control
actually results in the desired minimum value of the augmented performance index, a
second necessary condition is required. The strongest and most general mathematical
statement that guarantees minimal control is Pontryagin’s Minimum Principle [52], or
more succinctly, the Minimum Principle. In a summary of the Minimum Principle,
McShane [53] observes that the Hamiltonian, H , must be minimized over the set of all
possible u
. A rigorous proof of the theorem is offered by Pontryagin [54]. As a
consequence, consider the problem posed in the previous section with the same
assumptions. Then, the control ( )**u t ∈Ω that yields a minimum value of J requires
that the Hamiltonian ( ) ( ) ( )** **, , ,H X t u t t tχ
to be continuous on 0, ft t and,
( ) ( ) ( ) ( ) ( ) ( )** ** **, , , , , ,H X t u t t t H X t u t t tχ χ ≤
. (2.114)
44
Moreover, a second-order Taylor series expansion about u
demonstrates that if H is
differentiable in u
to second order (i.e., uuH exists), the optimal control at t is interior,
and ( )u t
is a weak variation, then,
( ) ( )0, 0u uuH t H t= ≥ , (2.115)
i.e., uuH must be positive semi-definite. This condition equivalently ensures the non-
negativity of the second differential on J . Equation (2.115) is known as the Legendre-
Clebsch necessary condition. Note that maximizing the performance index switches the
inequality sign in equations (2.114)-(2.115). Satisfying the Euler-Lagrange equations
and either the Minimum Principle or the Legendre-Clebsch condition forms two
necessary conditions, and, thus, a necessary and sufficient condition for a minimum value
of the augmented performance index. Given that the Euler-Lagrange equations are
satisfied, if only the Legendre-Clebsch necessary condition is satisfied, then only a weak
extremal [24] exists; thus, an unsatisfied Minimum Principle is still possible. In other
words, it is possible strong weakJ J′ ′< .
An alternative test for a minimum is the Weirstrass condition [25]. The Weirstrass
condition represents a more restricted version of the Minimum principle and is not
discussed here. Regardless of the second necessary condition, however, optimal control
yields only locally optimal solutions. Although different variations of the control result
in different classifications of the extremals, variations of the state must still remain
infinitesimally small.
45
3. LOW -THRUST TRANSFER ALGORITHM
In comparison to impulsive transfers, low-thrust transfers are typically characterized
by increased fuel economy at the expense of increased time of flight. However, an
additional difficulty is the determination of the control history, since an initially unknown
engine thrust direction and magnitude history is required continuously along the path.
For this investigation, invariant manifolds are used as an engine stopping condition, thus
incorporating a thrust-free arc along the transfer. Variable specific impulse (VSI)
engines are selected due to the advantage of a variable thrust profile. More robust
numerical convergence is also observed. To derive a steering law, optimal control theory
is applied, resulting in a primer vector law as detailed by Lawden [27] and Marec [32].
To meet the objectives of this analysis, it is not necessary to establish the complete two-
point boundary-value. (Not all natural boundary conditions are evaluated.) In fact, a
hybrid direct/indirect optimization scheme is employed. Nevertheless, many of the
benefits of optimal control theory are still employed. A direct minimization of the
performance index is attempted, while components of optimal control theory are utilized
to parameterize a control law.
The successful numerical computation of low-thrust transfers begins with a direct
shooting routine, using sequential quadratic programming (SQP) to update a search
vector. The sensitivities associated with this search vector and the nonlinear constraints
are evaluated numerically. Because the costates are so numerically sensitive, a useful
mapping process, the adjoint control transformation (ACT), is employed, transforming
several initial guess parameters into more physically realizable quantities. Despite this
mapping, the initial guess parameters are still extremely sensitive. The state and the
costate equations of motion are nonlinear and the propagation times are long (usually 20
46
days or more). To combat cumbersome blind initial guess strategies, a global set of
initial conditions over a bounded range is undertaken first. This “shotgun” step is
completed once to obtain a useful set of initial conditions for a desirable transfer. Once
accomplished, iteration with the local SQP routine proceeds to satisfy the kinematic
boundary conditions and achieve a stationary value, i.e., a local maximum, of the
performance index.
3.1 Engine Model
In developing low-thrust transfers, the most commonly applied engine model is the
constant specific impulse (CSI) engine. Such engines operate at maximum and minimum
(typically zero) thrust levels, at a fixed engine impulse, and yield the control law and
switching function detailed by Lawden [27]. These engine models have been exploited in
the investigations by Russell [44], Coverstone-Carroll and Williams [40], Sukhanov and
Eismont [41], Hiday [54], and many others. CSI engines find practical application in
many current low-thrust missions, for example ESA’s Smart-1 mission, that uses a solar
powered ion engine as shown in Figure 3.1 [55]. The determination of transfer
trajectories
Figure 3.1 CSI Engine Example – Smart-1 Ion Engine
47
transfer trajectories relies on the use of a switching function to govern engine on-and-off
times, frequently resulting in “bang-bang” control. In practice, the switching behavior is
difficult to predict given a set of initial conditions, and thus, a pre-determined switching
structure is often specified. Forcing a switching structure, such as a “thrust-coast-thrust”
profile implies that the two switching times must be incorporated as additional design
variables. Other investigators [45] seek to remove any forced switching behavior, and
predict the shape of the switching structure with the adjoint control transformation, and
an intuitive selection of the initial conditions. Such cases yield trajectories that include
several switching times, but are more sensitive to numerical convergence. There are also
other studies that reformulate the optimal control problem as a minimum time problem,
and assume an “always on” profile for the CSI engine.
Variable specific impulse (VSI) engines, as the name implies, operate under a
modulating specific impulse assumption. An example of such an engine is the Variable
Specific Impulse Magnetoplasma Rocket (VaSIMR), seen in Figure 3.2 [52]. While the
en
Figure 3.2 VSI Engine Example - VaSIMR Rocket
engine operates between a maximum and minimum power level, the varying impulse, Isp,
allows a thrust magnitude that potentially varies (≥ zero thrust) along the entire transfer
path. The assumption that the specific impulse, Isp, is unbounded allows the switching
function to completely vanish from the derivation of the control law. Without a
switching function present, additional variables in the search vector are not necessary.
48
Several applications that compare the two engine models, such as Ranieri and Ocampo
[36] and Sakai [57], note that VSI engines are capable of producing more efficient
transfers than CSI engines. In practice, the variable thrust profile also results in more
rapid numerical convergence than a fixed thrust magnitude. Variable specific impulse
engines are used here in response to the numerical sensitivity issues in the highly
nonlinear CR3BP; a potential improvement in fuel economy is also noted.
3.2 Control Law Derivation
A finite-thrust transfer, one with the greatest economy of fuel, and hence a minimum
expenditure of mass, is sought. To achieve this goal, a performance index is specified in
terms of a Mayer problem in the calculus of variations. The first necessary conditions are
derived using the Euler-Lagrange equations, as well as some of the natural boundary
conditions assuming a force model consistent with the CR3BP and variable specific
impulse (VSI) engines. Pontryagin’s Minimum Principle results in a steering law that is
Lawden’s primer vector, to be implemented as a steering parameterization at all times on
the extremals. The Minimum Principle, coupled with the Euler-Lagrange equations,
comprise a partial representation of the necessary and sufficient condition for a weak
relative minimum transfer trajectory. Due to the presence of VSI engines and a range on
specific impulse that is assumed to be unbounded, the switching function to govern the
thrusting profile vanishes upon inspection of the necessary conditions. The spacecraft is
assumed to originate on a circular parking orbit of fixed radius with respect to the Earth,
with the initial angular position, 0θ , utilized as a free variable. The spacecraft then
thrusts continuously until an insertion onto the stable manifold tube occurs. At this point,
the engines turn off and the spacecraft asymptotically converges into the desired
trajectory by means of the stable manifold associated with the target periodic orbit. The
transfer time and the insertion point along the manifold tube are also utilized as free
variables. Note that the optimal control formulation does not include a derivation of the
natural boundary conditions corresponding to the variables associated with the initial
49
departure angle on the parking orbit, or the variables that specify the state location on the
stable manifold.
The derivation of the control law begins by revisiting the general form of the
performance index, equation (2.104). Since the goal in the optimal control problem is to
maximize the final mass of the spacecraft arriving on the stable manifold, the function is
only dependent on endpoint conditions, and the resulting Mayer problem is
max ( )fJ k m= or min ( )fJ k m= − , (3.1)
where k is currently an undefined constant that rescales the problem. This constant will
serve a useful purpose later and does not alter the maximum mass goal.
Six controls, i.e., the scalar elements of the vector u
c , are used in the problem to
ensure a stationary value of the performance index: three thrust directions, ˆTu , the thrust
magnitude, T, the engine power, P, and a slack variable, σ , associated with maintaining
an engine power value within the prescribed bounds,
ˆTu
Tu
P
σ
=
c . (3.2)
The performance index is subject to dynamical, control, and endpoint constraints. The
dynamical constraints are comprised of the equations of motion in cartesian coordinates
and conservation of mass along the entire trajectory, defined as,
( ) ( ) ( ) ( )2
ˆ, ,
- 2n c T
v
X f X u t g r h v T m u
T P
= = + +
ɺ , (3.3)
where X
is the nx1 state vector (n = 7), defined by
r
X v
m
=
. (3.4)
The time invariant force field is defined consistent with the moon-centered CR3BP and
the associated dynamical equations of motion (equation (2.26)). The expressions are
50
decomposed into functions of position, ( )g r
, and velocity, ( )h v
as defined in equations
(2.27)-(2.28) .
The control constraints require the thrust direction, ˆTu , to be fixed on the unit sphere,
and engine thrust power to be bounded, i.e.,
ˆ ˆ 1TT Tu u = , (3.5)
2max sinP P σ= , (3.6)
where σ is the slack variable used to ensure to that max0 P P≤ ≤ . Note that this model
excludes the use of thrust and Isp constraints.
Since the state vector is comprised of 7 elements (n = 7), a total of 2n + 2 = 16
boundary conditions are necessary to formulate the complete two-point boundary-value
problem. The endpoint constraints are specified at the boundaries of the trajectory, with
the spacecraft originating at a planar circular parking orbit (recall 0r is fixed, but 0θ is
free), and initial constraints (equation (2.106)) evaluated as,
( )
( )( )
( )( ) ( )( ) ( )
( )( )
0 0 0
0 0 0
0
0 0 0 0 0
1 0 0
0 0 0 0 0
0
0 0
0
cos
sin
sin sin, 0
cos cos
I Sx
nI S
y
z
x t r
y t r
z t
v t r rt X
v t r r
v t
m t m
t
θθ
µ θ ω θϕ
µ θ ω θ
⊕
+
⊕
− −
− − + = = − − −
, (3.7)
Equation (3.7) adds (n + 1) boundary conditions, with 8 yet required. The spacecraft
terminates at a p x 1 target vector, ( )p fXψ
, where p = 6, on the stable manifold,
( ) ( ) ( )( ) ( )
,0
,
f M M M
p f
f M M M
r t rX
v t v
θ τψ
θ τ
− = = −
, (3.8)
where the position and velocity states, ( ),M M Mr θ τ, and ( ),M M Mv θ τ
are states along the
manifold tube parameterized by the free angle-like variable Mθ , and the free time-like
51
variable Mτ , as shown in Figure 3.3. The angle-like variable specifies the stable
manifold trajectory given a fixed-point (red) along the libration point orbit (pink), and the
time-like variable specifies the state at a given time along a specified stable manifold
trajectory (blue). Since ( )p fXψ
provides p = 6 boundary conditions, two more
boundary conditions (one associated with the final mass, and one associated with the final
time) are required for the TPBVP. At this point, the augmented cost function is
introduced by adjoining the two Lagrange multipliers vectors, Tϖ , and υΤΤΤΤ to the end-
point constraints, a Lagrange multiplier vector η to the control constraints, and adding
the result to the original performance index,
( )0
ˆft
T
t
J H X dtχ′ = Θ + −∫ ɺ , (3.9)
where,
( )( ) ( )( )0 0,Tf fkm X t t X tϖ ϕ υ ψΤΘ = +
++++ , (3.10)
( ) ( )21 2 max
ˆ ˆ ˆ 1 sinTH H u u P Pη η σ= + − + − . (3.11)
Recall that if all the constraints are satisfied, minimizing the augmented performance
index is identical to minimizing the actual performance index. The extended
Hamiltonian, H , is then the path-wise analog of adjoining Lagrange multipliers to the
endpoint function, Θ . (See Hull [25] for further details.)
Figure 3.3 Behavior of θΜ and τM Along the Stable Manifold Tube
( ),i jM MMX θ τ
( )1,
i jM M MX θ τ+
( )1,
ji MM MX τθ+
( )1 1,
i jM M MX τθ+ +
( )2,
ji MM MX τθ+
( )1 2,
i jM M MX τθ+ +
52
To satisfy the first necessary condition for a maximum value of the performance index,
it is required that the differential of the augmented cost function, dJ′ , vanish. The
expression 0dJ′ = leads to the Euler-Lagrange equations (equations (2.111)-(2.113)),
applied to the extended Hamiltonian, and the natural boundary conditions (equations
(2.115),
( )ˆ , , ,TcX
H X u tχ χ= − ɺ , (3.12)
( )ˆ , , , 0c
Tu cH X u tχ =
, (3.13)
( ) ( )( )00 0 0, , , , ,T
f fXX t t X t tχ ϖ υ= −Θ
, (3.14)
( ) ( )( )0 0, , , , ,f
Tf f fX
X t t X t tχ ϖ υ= Θ
, (3.15)
( ) ( )( )0 0, , , , ,f
Tf t f fH X t t X t t ϖ υ= −Θ
. (3.16)
The natural boundary conditions provide the additional 2 boundary conditions, since
1 2n p+ − = . Solving the Euler-Lagrange equations requires the formation of the
Hamiltonian, as expressed in equation (2.108). Since the performance index is a Mayer
problem, the Hamiltonian becomes,
( ) ( ) ( )( ) ( )2ˆ 2T T Tr v T mH X v g r h v T m u T Pχ χ χ χ= = + + + −
ɺ . (3.17)
Equation (3.11) is then used to evaluate equation (3.12), yielding the costate equations of
motion,
( )T
r vtχ χ= −B ɺ , (3.18)
Tv r vχ χ χ= − − C ɺ , (3.19)
( )2 ˆTm r TT m uχ χ=
ɺ , (3.20)
where B(t) and C are defined in equations (2.34)-(2.35). Note that these matrices are
simply ( )g r
r
∂∂
and ( )h v
v
∂∂
, respectively. Expanding equation (3.13) yields
1
ˆˆ2 0
T
v TT
HT m u
uχ η
∂ = + = ∂
, (3.21)
53
ˆˆ 0
T
Tv T m
Hu m T P
Tχ χ
∂ = − = ∂
, (3.22)
2 22
ˆ0
T
m
HT P
Pχ η
∂ = + = ∂ , (3.23)
2 max
ˆ2 sin cos 0
TH
Pη σ σσ
∂ = − = ∂ . (3.24)
Equation (3.21) implies that either vχ is parallel to Tu , T and 1η are both zero, or vχ and
1η are both zero. Since the latter two cases will rarely apply in any practical problem, vχ
is assumed to be always parallel to ˆTu . As a result, two possible solutions for ˆTu emerge,
ˆT v vu χ χ= ± . (3.25)
Equation (3.22) can also be solved directly to yield,
v
m
PT
m
χχ
=
. (3.26)
From equations (3.23)-(3.24), either 2η , cosσ , or sinσ must be zero. Inspection of
equation (3.6) suggests the possibilities: (i) if cosσ = 0, then P = Pmax; (ii) if sinσ = 0,
then P = 0; and, (iii) if 2 0η = , then max0 P P≤ ≤ .
Next, the remaining two natural boundary conditions (n + 1 – p = 2) are determined via
equations (2.113),
0 0 00 0
T TT T T Tr v m r v mX χ χ χ χ ϖ ϖ ϖ −∂Θ ∂ = → = −
, (3.27)
f f f
T TT T T Tf f r v m r vX kχ χ χ χ υ υ ∂Θ ∂ = → =
, (3.28)
0fH = . (3.29)
The constant, k, in equation (3.28) serves as the only non-trivial value determined from
equations (3.27)-(3.28), and the constraint on the final value of the Hamiltonian in
equation (3.29) provides the last boundary condition. The unknown initial mass costate,
0mχ , may now be completely removed from the problem. Observing that the mass
costate monotonically increases in equation (3.20), that is, any initial value, 0m mχ ϖ= ,
54
allows the final costate value fmχ to approach the positive constant value k. Thus, the
initial mass costate may be arbitrarily fixed as unity,
01mχ = . (3.30)
Although equation (3.29) is the final condition required for a fully specified TPBVP, it
will later be ignored when a hybrid direct/indirect solution method is established.
Pontryagin’s Minimum Principle is applied as a second necessary condition, and thus, the
sufficient condition for a local maximum.
For this specific problem, equation (2.114) is reduced to the following:
( )( ) ( ) ( )( ) ( )** ** **2 ** 2ˆ ˆ2 2T Tv T m v T mT m u T P T m u T Pχ χ χ χ− ≥ −
. (3.31)
It is clear from the above expression that a positive sign on Tu in equation (3.25) must
occur for the inequality to achieve a maximum value, resulting in the definition of p
, the
primer vector. The associated primer vector control law, as observed by Lawden [27], is
expressed,
ˆT v vu p pλ λ= ≡
. (3.32)
The structure of this control law implies that the thrust is always in a direction along the
primer vector. A physical interpretation of this optimal control law is that the thrust
acceleration is always directed toward a neighboring point (also in motion) subject to the
same gravitational field and thrust acceleration as the spacecraft.
The observations (i)-(iii) on the engine power, P, resulting from equations (3.23) and
(3.24) are further reduced by substituting equation (3.26) and equation (3.32) into
equation (3.31),
2 ** 2
2 22 2v v
m m
P P
m m
χ χχ χ
≥
, (3.33)
that yields two possible values of P,
P = Pmax, 0mχ ≥ , (3.34)
P = 0, 0mχ < , (3.35)
But, since the first necessary conditions identified a value mχ that begins at one and
monotonically increases, equation (3.35) is never possible for this problem. Thus,
55
equation (3.34) is exclusively employed, so the engine will always operate at maximum
power, Pmax. The control law for the power, equation (3.34), automatically satisfies
equations (3.23)-(3.24) when cosσ is always zero. This requirement also reduces
equation (3.26) such that,
maxv
m
PT
m
χχ
=
. (3.36)
Four results that are that necessary to ensure a local maximum value of the
performance index emerge, and are summarized as follows:
1. The thrust direction is always tangential to the primer vector, i.e.,
ˆT v vu p pχ χ= ≡
.
2. There is no requirement for a switching function on any of the controls, the
engine always operates at maximum power, i.e.,
maxP P= .
3. The initial mass multiplier always equals unity, 0mχ = 1, and monotonically
increases to reach the arbitrary final mass multiplier fmχ = k.
4. The thrust magnitude is always defined as
maxv
m
PT
m
χχ
=
.
These four conditions ultimately comprise the “indirect” components in the formulation
of a hybrid direct/indirect numerical solution.
3.3 Adjoint Control Transformation
Not surprisingly, one of the most difficult aspects in obtaining a solution to a trajectory
optimization problem that uses elements of optimal control theory and the associated
TPBVP, is generating an accurate initial guess for the costates. Low-thrust problems are
also typically characterized by long propagation times, rendering the problem even more
sensitive to initial conditions. Typical strategies to address the initial guess dilemma
56
often solve several smaller sub-problems [29-30] or use analytical results [44]. An
alternate approach, first investigated by Dixon and Biggs [58], introduces physical
control variables and their derivatives as an estimate of the initial costates. These control
variables allow exploitation of physical intuition to produce a guess for the values of the
initial costate variables. Such insight can reduce the problem sensitivity.
Consider a reference frame centered at the spacecraft, defined by the unit vectors
ˆˆ ˆv w h− − . The v -axis of this frame is aligned with the relative velocity vector, v
. The
h -axis is aligned with the instantaneous angular momentum vector, h
. Finally, the w -
axis is defined to complete a right-handed system. These unit vectors, and associated
time derivatives that create this frame, are defined as,
ˆv
vv
=
, ˆ r vh
r v
×=×
, ˆˆ ˆw h v= × , (3.37)
2
2
ˆ ,
ˆ ,
ˆ ˆˆ ˆ ˆ.
v v v vv v
h h h hh h
w h v h v
= −
= −
= × + ×
ɺ ɺ ɺ ɺ ɺ ɺ
ɺɺ ɺ
(3.38)
Given a vector and its time derivative, the following relationships are used to fully
determine equation (3.38),
,
.
v v v v
h h h h
= ⋅
= ⋅
ɺɺ
ɺ ɺ (3.39)
As is apparent in Figure 3.4, two spherical angles, α , β , and their time derivatives αɺ
and βɺ specify the orientation of the thrust direction relative to this frame, vwhTu , and also
the time derivative of the thrust direction, ˆvwhTuɺ , that is,
[ ]ˆ cos cos sin cos sinvwh
T
Tu = α β α β β , (3.40)
sin cos cos sin
ˆ cos cos sin sin
cosvwhTu =
α α β β α βα α β β α β
β β
− − −
ɺɺ
ɺ ɺɺ
ɺ. (3.41)
57
However, since the equations of motion are integrated in the cartesian, moon-centered
rotating frame (with unit vectors ˆˆ ˆi j k− − ), a rotation matrix, R, is required to transform
the thrust direction, vwhTu (and ˆ
vwhTuɺ ),
ˆˆ ˆ ˆˆ ˆ
ˆˆ ˆ ˆˆ ˆ
ˆ ˆ ˆ ˆˆ ˆ
i v i w i h
j v j w j h
k v k w k h
⋅ ⋅ ⋅
= ⋅ ⋅ ⋅
⋅ ⋅ ⋅
R , (3.42)
ˆˆ ˆ ˆˆ ˆ
ˆˆ ˆ ˆˆ ˆ
ˆ ˆ ˆ ˆˆ ˆ
i v i w i h
j v j w j h
k v k w k h
⋅ ⋅ ⋅ = ⋅ ⋅ ⋅ ⋅ ⋅ ⋅
R
ɺɺ ɺ
ɺɺ ɺɺ
ɺɺ ɺ
, (3.43)
ˆ ˆijk vwhT Tu = uR , (3.44)
ˆ ˆ ˆijk vwh vwhT T Tu = u u+R Rɺ ɺɺ . (3.45)
The direction, ijkTu , denotes the thrust direction as expressed in terms of unit vectors in
the moon-centered, rotating frame. The definition of the primer vector, p
, from equation
(3.32), is employed to parameterize the velocity costate vector,
ijkv v Tp uχ χ= =
, (3.46)
where vχ is the magnitude of the velocity costate, v vχ χ=
. The equation of motion for
the velocity costate, equation (3.19), is directly involved in parameterizing the position
costate vector,
Tr v vχ χ χ= − − C ɺ . (3.47)
The derivative of the velocity costate vector, vχ ɺ , is available by differentiating equation
(3.46), and substituting the result into equation (3.47), to yield an expanded version of
rχ ,
ijk ijk
Tr v T v T vu uχ χ χ χ= + − C ɺɺ . (3.48)
58
The magnitude of the velocity costate time derivative vector, vχ ɺ , is approximated by
assuming an initial value of the Hamiltonian, H0 = 0, substituting equation (3.36) into
equation (3.17), and rearranging,
( ) ( )( )( )1ijk
ijk
T Tv v T v v
T
u v v g r h vu
χ χ χ χ= + − +C
ɺɺ . (3.49)
Additionally, equation (3.36) is used to parameterize vχ in terms of the thrust, T. Thus,
an important mapping sequence ( ) ( ), , , , ,r vTα α β β χ χ→ ɺɺM is now available, where it
is noted that the dimension of the initial conditions in the mapping are reduced by one
through the assumption on H0. (A similar implementation of this step is provided by
Senent et al. [28].) Rather than an initial guess that requires explicit values of the
position and velocity costates, the physically meaningful quantities , , , , and Tα α β βɺɺ ,
determined via equations (3.36)-(3.49), are a practical alternative.
Figure 3.4 Velocity Reference Frame
v
h
w
ˆTu
αβ
βɺ
αɺ
59
3.4 Numerical Solution via Direct Shooting: A Local Approach
An algorithm is constructed to iteratively solve the optimization problem by direct
shooting, while incorporating components of the indirect TPBVP. For the actual
numerical process, a new state vector, ( )Y t
is defined with elements that correspond to
the states and the costates. The governing differential equations then appear in the
following form,
( ) ( )( ) ( ) ( )
( )
( )
2
2
ˆ
2
ˆ
T
T
vrT
r vvTr Tm
vrg r h v T m uv
T PmY t f Y
t
T m u
χχχ χχ
χχ
+ + − = = = − − −
B
C
ɺ ɺ
ɺ ɺɺ
ɺ
ɺ
. (3.50)
Note that the new vector Y
, is comprised of 14 states. However, for the transfer
problem, an initial numerical search vector, S′
is composed of 9 design variables: 5
variables for the adjoint control transformation; one variable for the initial parking orbit
angular position,0θ ; one variable corresponding to the final time, tf ; the angle-like
variable, Mθ , along the manifold tube; and, the time-like variable, Mτ , along the
manifold tube. The sensitivities of 0θ , Mθ , and Mτ are acquired completely numerically,
and are not available in closed form. The inclusion of these three additional search
variables in S′
is not formulated in the model of the indirect TPBVP. If these additional
variables ( 0θ , Mθ , Mτ ) are to be incorporated in the formulation, additional criteria are
required to produce additional natural boundary conditions. In practice, the absence of
the other natural boundary conditions in the TPBVP (including equation (3.29)) is offset
by direct iteration on the performance index. The initial search vector, S′
is defined,
60
0
0
f
M
M
S
T
t
θααββ
θτ
′ =
ɺ
ɺ , (9 x 1), (3.51)
where the mapping, ( ) ( )0 00, , , , ,r vTα α β β χ χ→ ɺɺM is supplied only once to determine the
initial value of the elements in the actual (10 x 1) search vector, S
,
0
0
0
0
0
0
0
x
y
z
x
y
z
v
v
v
f
M
M
S
t
θχχχχ
χ
χ
θτ
=
, (10 x 1). (3.52)
Subsequently, an iteration process is incorporated to update the search vector such that
the kinematic boundary conditions are all satisfied. In the numerical algorithm, the
kinematic boundary conditions specify the entire constraint vector, c
, i.e.,
( ) ( ) ( )( ) ( )
,, 0
,
f M M M
f f
f M M M
r t rc X t
v t v
θ τψ
θ τ
− = = −
= , (6 x 1). (3.53)
A nonlinear programming algorithm, based on medium-scale Sequential Quadratic
Programming (SQP), using “fmincon” in MATLAB®, updates the values of the 10 design
variables in S
to resolve any potential constraint violations in c
. In this particular
scheme, the optimizer solves a quadratic programming (QP) sub-problem every iteration,
61
and computes a quasi-Newton approximation of the Hessian of the Lagrangian using the
Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula [59]. A medium-scale algorithm is
used due to the nonlinear constraints. All gradient information is approximated
numerically. This problem might benefit from analytical gradients, although
recalculation is required any time the problem is slightly reformulated.
This solution scheme is termed a hybrid direct/indirect method since the performance
index is minimized directly, while elements of the indirect optimal control problem are
still employed. Thus, formulation of all the natural boundary conditions is ignored, and a
stationary value of the performance index is detected numerically, effectively replacing a
condition that could only be satisfied through the solution of the complete TPBVP. An
objective function, Q, is supplied to the numerical optimizer to be the equal to the
performance index, – J, i.e.,
fQ J m= − = − . (3.54)
The flow chart in Figure 3.5 represents the details of the numerical algorithm that is
implemented to determine the locally optimal solutions.
62
Figure 3.5 Numerical Algorithm – Direct Shooting Method via SQP
0 0 0 0 0 00 x y z
T
x y z v v v f M MS tθ χ χ χ χ χ χ θ τ=
SQP ROUTINE SQP ROUTINE
Initialize 0Y
Propagate
equations (3.50)
forward to time tf,
objective function,
Q, (equation(3.54)),
is computed,
fQ J m= − = −
Using equation (3.8),
compute terminal
constraint values,
fψ
NO YES
Optimal S
is stored for plotting and
future continuation processes
Optimizer performs
several small updates
on S
to estimate
numerical gradients,
and then SQP
algorithm updates S
Is Q locally minimal
and f toleranceψ ≤ ?
Mapping ( ) ( )0 00 0 0 0 0 0, , , , , ,r vTθ α α β β χ χ→ ɺɺM using
equations (3.35)-(3.34)
Guess 0 0 0 0 0 0
T
f M MS T tθ α α β β θ τ′ =
ɺ
63
3.5 Shotgun Method for Initial Conditions: A Global App roach
Despite the added benefits of the adjoint control transformation, many initial guesses
are typically required to determine a vector S
within the convergence radius of the local
SQP optimizer. To avoid a cumbersome manual search for appropriate initial conditions,
a numerical, low-fidelity, automated process is developed to establish a “global” set of
initial conditions. Such a step is intended to precede any SQP optimization attempt.
Upper and lower bounds on each design variable in the initial search vector,S′
, are
selected, except the variables ft , Mθ , and Mτ . These three design variables are selected
differently. For the first six elements of S′
, the variables are each within the bounds
corresponding to the individual global ranges, i.e.,
0 0
0 0
1,...,
1,...,
1,...,
,
,
,
,
,
,
LB UB
LB UB
h
i
j
LB UB
LB UB
LB UB
LB UB
g
f h d
M i a
Mj b
S T T
t
θ θα αα αβ ββ β
θ
τ
=
=
=
′ =
ɺ ɺ
ɺ ɺ, (3.55)
where the subscript “LB” denotes the lowest possible value on design variable and “UB”
denotes the highest possible value. Thus, the first six design variables may fluctuate.
The propagation time is always initially selected as a maximum value, fixedft = td
(typically 50 days), although samples over the entire range are subsequently tested. The
full range of the angle-like parameters, 1,...,i aMθ
= (of length a), and time-like parameters,
1,...,j bMτ=
(of length b) are always initialized. (See Figure 3.3.) The first 7 variables in S′
allow Y
to be propagated forward for the fixed length specified for the maximum time
64
interval, fixedft . Note that any discretized point along the stable manifold is a potential
“match” point, since every combination of the angle-like and time-like variables 1,...,i nMθ
=
and 1,...,j mMτ
= is utilized. From this pool of starting values, representative combinations are
sampled. All elements in gS′
are first fixed; hf
t is sampled through each time element,
0,..., h d= , over a propagation of Y
. Along each time step, hf
t , each possible
combination of the current kinematic boundary condition errors along the stable manifold
tube, hijψ , are computed,
( ) ( )( ) ( )
,
,
h i j
h i j
f M M M
hij
f M M M
r t r
v t v
θ τψ
θ τ
− = −
. (3.56)
For each initial state, the terminal error vector along the trajectory, hijψ , with the lowest
norm is then stored. Another candidate S′
within the desired bounds of gS′
is then
propagated, and the process is repeated. Once a pre-determined number of S′
combinations is propagated, only the five search vectors with the lowest error norm,
hijψ , are selected for attempted convergence in the local SQP algorithm represented in
Figure 3.5. Note that once the indices, h, i, j, that identify the lowest error vector, hijψ ,
are isolated, a single S′
vector is available for use in the local method, (i.e., tf = ht ,
iM Mθ θ= , jM Mτ τ= ). This process is similar to the first level of a global parameter
optimization scheme that establishes a “population”, such as a genetic algorithm. It is
also analogous to propagating a representative set of “open loop” initial conditions since
no iterative procedures are occurring, merely propagation. A random number seed is
incorporated to propagate a specific population among the bounded variables that is
repeatable. Thus, this step is loosely regarded as the first level of a “hybrid” parameter
optimization sequence (not to be confused with the hybrid direct/indirect trajectory
optimization method), where the best condition(s) are supplied to the “closed-loop” local
SQP method. For an example involving a 12-day halo orbit (section 4.2.1), sample
65
populations for , , , and α β α βɺɺ appear in Figure 3.6. Note that a uniform random
distribution is employed.
Bounds: [ ] [ ] [ ] [ ]0 0 0 00, 0.2 , 0.3, 0.3 , 0.02, 0.02 , 0.3 0.3α β α β= = − = − = −ɺɺ
Population Size: 500
Figure 3.6 Example Population Parameters for a 12-Day Halo Orbit
66
4. MISSION APPLICATIONS
The free final time targeter in equation (2.80) is used to generate families of periodic
orbits. These families include L1 and L2 halo orbits, L1 and L2 vertical orbits, and L2
“butterfly” orbits. All of the families of orbits are generated to meet a basic altitude
constraint. The trajectory design process involves multiple spacecraft in combinations of
potentially different orbits to meet the design objective, that is, nearly continuous line-of-
sight coverage of the lunar south pole. Analysis of periodicity and orbital stability yields
individual orbit selection criteria. Many of these orbits have already been incorporated
into a more rigorous coverage and stationkeeping analysis in a full ephemeris model [46].
Once identified as a viable option, transfers to these orbits are required. Investigations of
transfers from Earth into such orbits are limited. Low-thrust transfer trajectories to these
orbits are the focus here.
The algorithm for the design of low-thrust transfers is applied to a mission scenario
that requires the delivery of a spacecraft into an orbit for potential lunar south pole
coverage. All transfers are propagated within the P2-centered Earth-moon CR3BP
(equations (3.3)) to reduce numerical sensitivities in the vicinity of the insertion point.
Since the invariant manifolds, like the associated orbits, are generated in the barycentric
frame associated with equations (2.15)-(2.17), a coordinate transformation aligns the axes
to the P2-centered frame. As assumed in Chapter 3, a spacecraft originates at an
unspecified departure angle in a circular parking orbit about the Earth. It then reaches an
unspecified location along the stable manifold tube, and inserts into a manifold trajectory.
The invariant manifold coast includes a significant length of time that the engine power
remains off. The local hybrid direct/indirect optimization process results in locally
optimal transfer trajectories. When necessary, the global “shotgun” method is used to
produce initial conditions within the convergence radius of the direct shooting routine.
67
4.1 Orbits for Line-of-Sight Lunar South Pole Coverage (CR3BP)
Due to the potential existence of frozen volatiles [60,61], one current location of
interest for future space exploration is the region near the lunar south pole. This goal has
been identified in the President’s Vision for Space Exploration announcement in January
2004, as NASA indicates that water ice at the lunar poles may help facilitate exploration
of the solar system [62]. NASA’s Exploration Communication and Navigation Systems
(ECANS) Team, specifically the Lunar Communications and Navigation Systems
(LCNS) group, is interested in spacecraft architectures for communications with ground
stations on the lunar surface. Such a ground station on the moon would benefit from a
system of satellites that are always within direct view of the Earth and that provide
constant communications between the lunar surface and the Earth. Various CR3BP
orbits are potentially applicable in mission design of lunar relay communication satellites
for lunar coverage due to the fixed geometry in the rotating frame and line-of-sight
capability. For example, L1 and L2 southern halo orbits possess a line-of-sight with the
lunar south pole over the majority of the orbital period, and a line-of-sight with the Earth
for the entire orbital period. Additional orbital information on the periods and stability
indices aids in the selection of specific orbits.
4.1.1 Three-Dimensional Periodic Orbits in the CR3BP
Similar to the two-dimensional, Lyapunov example from Section 2.2.4, periodic orbits
are determined with the free final time targeting algorithm (equation (2.80)). Depending
on the specific type of orbit, different control parameters are employed, and different
states serve as targets to successfully converge on an initial condition that yields a
periodic orbit. (Recall that the term “control”, used in this context, simply refers to
parameters that may vary in targeting periodic orbits.) All orbits generated share a
symmetry across the x-z plane. Thus, an initial state vector is aligned on the x-y plane,
with nonzero elements in x , z, and yɺ only, such that,
68
0 0 0 00 0 0T
X x z y=
ɺ . (4.1)
The variational targeting equation (equation (2.80)), is now reduced to,
0
0
00 0 0
f f ff f
x
zX X XX X
yx z y
δδ
δδδτ
∂ ∂ ∂ = ∂ ∂ ∂
M
ɺ
ɺɺ
, (4.2)
where M is a 3 x 4 matrix. The three elements of the target vector, fXδ
, are selected
depending on the type of orbit. From observation of equation (4.2), there are four
possible controls. In a process similar to that for Lyapunov orbits, one nonzero initial
parameter is be fixed, while the other two (and time) are allowed to vary as control
parameters. Once a periodic solution is available, a method of continuation is applied to
create a new orbit. The targeting process is repeated to generate successive orbits in the
family. A description of the full targeting scheme is presented in Grebow [63].
4.1.2 Families of Orbits for Lunar South Pole Coverage
Families of orbits for potential application in the problem of lunar south pole coverage
are obtained with lunar altitudes between 50 km and 100,000 km. The maximum bound
is assumed as a communications instrument constraint; the minimum bound is selected
arbitrarily to avoid a subsurface arc. Orbits within the acceptable range from the L1 and
L2 southern halo orbit families appear in the moon-centered frame in Figure 4.1 [46,63].
The halo orbits (a term first used by Farquhar [64]), bifurcate from both the L1 and L2
Lyapunov family of orbits, and resemble a halo-shape about the moon when viewed from
the Earth in the rotating frame. As previously stated, the orbits are particularly effective
in the lunar south pole coverage problem since the motion is almost always within line-
of-of sight to the Earth. The family is composed of halo orbits that resemble the
traditional “halo” shape in addition to highly “elliptic”, near-rectilinear orbits with
passage very close to the moon’s surface. For almost the entire period of motion, a
spacecraft in any near-rectilinear halo orbit possesses a line-of-sight to the lunar south
69
pole. Most recently, the halo orbit families have been thoroughly investigated by
Farquhar [64], Breakwell and Brown [17], Howell [18], and Gómez et al. [65].
Members of the southern L1 and L2 vertical orbit family are depicted in Figure 4.2
[46,63]. The motion consists of a doubly symmetric, “figure-8” shaped pattern when
viewed in the y-z plane. These orbits occur near the libration points. The existence of
these orbits was predicted by Moulton in 1920 [14], and have also been studied recently
by Dichman et al. [21]. Large amplitude L1 vertical orbits terminate when they become
exactly vertical, while large amplitude L2 vertical orbits encompass both primaries
(although these trajectories are not included due to the mission constraints). The orbits
also possess the characteristic of bending toward both the north and south poles of the
moon, a favorable trait for maintaining line-of-sight over a pole.
An additional family also includes orbits that remain in view of the lunar south pole for
significant intervals of time. Some of these orbits possess characteristics similar to the
near-rectilinear halo orbits. The orbits bifurcate from a 6-day near-rectilinear L2 halo
orbit and might be described as a “butterfly” shape. (See Figure 4.3 [46,53]).
Comparable motions around the smaller primary have been documented by Robin and
Markellos [66]. Similar to vertical orbits, the motion in a butterfly orbit resembles a
“figure-8” shape, however, these orbits wrap around both the near and far side of the
moon, such that a direct line-of-sight to the lunar south pole exists for nearly the entire
orbital period.
70
Figure 4.1 Southern Halo Orbit Families: Earth-Moon L1 (Orange) and L2 (Blue); Moon Centered, Rotating Reference Frame
Moon
To Earth
Moon
To Earth
71
Figure 4.2 Vertical Orbit Family of Interest: Earth-Moon L1 (Magenta) and L2 (Cyan); Moon Centered, Rotating Reference Frame
Moon
To Earth
To Earth
Moon
72
Figure 4.3 Southern L2 Butterfly Orbit Family; Moon Centered, Rotating Reference Frame
Moon
To Earth
Moon
To Earth
73
4.1.3 Mission Orbit Selection Criteria
The time to complete one full period is used as a design parameter for orbit selection to
be applied in the coverage problem. Let the maximum excursion distance identify a
particular orbit, as indicated using a halo orbit in Figure 4.4 [46,63] (right). Maximum
excursion distance is defined as the maximum x-distance for each orbit in the moon
centered, rotating frame. In Figure 4.4 [46,63] (left), orbital periods are plotted against
maximum excursion distance during initial design selection. Commensurate orbits are
sought to phase multiple spacecraft for complete line-of-sight coverage. One such region
might consist of orbits in L1 and L2 halo families sharing periods between 7.9 and 12.2
days. An example that exhibits feasible south pole coverage is a 12-day L1 and 12-day L2
halo orbit combination, illustrated by the black dashed line in Figure 4.4. Another region
with commensurate combinations consists of orbits with a ratio of periods equal to 2:1,
that is, one period is exactly twice that of the other. Note that L2 halo orbits with periods
between 6.0 and 7.2 days exhibit this behavior with the entire L2 butterfly orbit family.
This is not actually surprising when the shapes of the orbits are viewed in Figure 4.1 and
Figure 4.3. An example from this region consists of a 14-day L2 butterfly orbit and a 7-
day L2 halo orbit combination, as noted by the two red dashed lines in Figure 4.4. The
information in Figure 4.4 serves as a basis for the determination of many other
commensurate orbit combinations that lead to complete south pole coverage.
Also useful for design purposes is the stability index, . .S I The stability index,
corresponding to one orbit period, IP, is defined as,
maxmax
1 1. .
2S I λ
λ
= +
, (4.3)
where maxλ is the maximum eigenvalue from the monodromy matrix, ( ),IP t tΦ + ,
computed at the end of one revolution. A stability index of one indicates a stable orbit,
whereas stability indices greater than one reflect instability. Of course, a large stability
index indicates a divergent mode that departs from the vicinity of the orbit very quickly.
Generally, the stability index is directly correlated to the station-keeping costs and is
inversely related to transfer costs. The stability indices for orbits from the various
74
families appear in Figure 4.5 as functions of the maximum excursion distance from the
moon. In general, the stability index increases with maximum excursion distance from
the moon.
Once the orbits of interest have been selected to yield the appropriate coverage of the
lunar south pole, transfers to deliver the spacecraft into such orbits must also be available.
An analysis of the coverage schemes is discussed in references [42,59]. Within the
context of the multi-body problem, the stability index must be sufficiently large to
produce stable manifold trajectories that arrive at the orbits during numerical simulations.
As stability indices approach a value of one (a stable orbit), the manifolds become more
difficult to produce numerically due to the increasingly stable behavior within the phase
space. In some situations, this complexity is offset by increasing the initial orbital
displacement distance, d, described in Section 2.3.3. In general, such orbits are excluded
from this investigation. As a result, because the 6-day and 7-day halo orbits possess
stability indices very close to one, they are not considered for use in this transfer scheme.
Figure 4.4 Period versus Maximum x-Distance from the Moon (Left); Definition of Maximum x-Distance (Right)
x
z L1 Halo
Orbit Example
Moon
Max |x|
14-day L2 butterfly and 7-day
L2 halo orbit combination
12-day L1 halo and 12-day L2
halo orbit combination
75
Figure 4.5 Stability Index versus Maximum x-Distance from the Moon
4.2 Optimal Transfers to the Earth-Moon Stable Manifold
For the design of low-thrust transfers to orbits for lunar south pole coverage, a number
of specific scenarios are identified. Due to the numerical sensitivities in the
determination of three-dimensional transfers in the CR3BP model, the design process for
the low-thrust transfers includes the global and local solution method detailed in Chapter
3. At the initiation of the local SQP routine, a hybrid direct/indirect method reduces the
objective function and satisfies the kinematic boundary conditions. The fixed dynamical
and propulsion constants for all scenarios are listed in Table 4.1. An initial spacecraft
mass is assumed at 1,500 kg, with VSI engines capable of delivering a maximum engine
power of 10 kw. For all simulations with low-thrust acceleration terms, the differential
equations that model the system are the P2-centered, i.e., moon-centered equations of
motion, as detailed in equations (2.26)-(2.31), and in equation (3.3) with the associated
76
thrust terms. Additionally, these nondimensional equations include a scale factor on the
initial spacecraft mass such that it is equal to 1 in the numerical process, thus avoiding
scaling issues that arise from the magnitude of the characteristic Earth-moon mass, m*.
Any simulations using the barycenteric equations of motion, e.g., computation of the
orbits and the stable manifolds, are shifted into moon-centered coordinates as well.
A fixed, circular orbit parking radius of 20,000 km is established for all low-thrust
transfer examples. Of course, a wide range of Earth orbits may serve as the departure
orbit. For convenience, it is assumed that the departure orbit is circular. Parking orbits at
radii lower than 20,000 km result in long integration times and numerical sensitivity
issues. Of course, these numerical computations can be offset by establishing a parking
orbit at LEO and utilizing higher initial thrust values. But the larger radius was selected
to demonstrate the capability of engine parameters previously examined [28].
Nevertheless, using the same solution method, a fuel optimal, planar, circle-to-circle
transfer is determined, as seen in Figure 4.6, to achieve optimal orbit raising from LEO
(200 km altitude; dotted red line in Figure 4.6) to the nominal departure orbit at radius
20,000 km (dotted green line in Figure 4.6). Obviously, the v∆ required to reach the
20,000 km radius departure is dependent on the initial parking orbit supplied from a
launch vehicle. The value of 3.21 km/s for VSIv∆ represents the cost in terms of an
equivalent maneuver magnitude using VSI engines to reach the nominal departure orbit.
For comparison, a transfer arc modeled as a two-burn Hohmann transfer requires a Hv∆
value of 3.09 km/s.
77
Table 4.1 Dynamical and Propulsion Constants
Parameter Value Units
Circular Parking Orbit Radius 20000 km
m0 1500 kg
Pmax 10 kw
m* 6.04680403834987 x 1015 kg
l* 385692.5 km
t* 377084.152667039 sec
Earth GM 398600.432896939 km3/s2
Moon GM 4902.80058214776 km3/s2
Earth Radius 6378.14 km
Moon Radius 1737.4 km
VSIv∆ From LEO to Parking Orbit 3.206418 km/s
Hv∆ From LEO to Parking Orbit 3.088713 km/s
Figure 4.6 Optimal Orbit Raising from LEO
78
4.2.1 Transfers to a 12-Day L1 Halo Orbit
4.2.1.1 “Short” Transfer
For the first demonstration of a free final time, optimal, low-thrust transfer, a 12-day L1
halo orbit is selected. As noted in Figure 4.1, there are two different 12-day L1 halo
orbits that may be used. The lower z-amplitude orbit (Az = 13,200 km) is selected for this
example to reduce the sensitivity issues on the out-of-plane costates. The orbit, and the
associated stable manifold tube, are depicted in Figure 4.7. The stable manifold (green)
is propagated (backwards in time) for two successive Earth flybys; each flyby is
recognized during the simulation by the point at which the flight-path-angle changes sign.
The manifold tube is then parameterized in terms of 50 state vectors, ( )s iX t
. Each
( )s iX t
(nfp = 50), calculated from equation (2.102), is propagated backwards as detailed
in Section 2.3.3. Recall that each fixed point M is associated with one trajectory along
the manifold. So, the angle-like parameterization, θM, tags a particular fixed point and,
thus, a specific manifold trajectory. The time-like parameter, τM, corresponds to a time-
index along the specified manifold trajectory. In this case, 0 50Mθ≤ ≤ (50 fixed-points
equally spaced in time), and 0 2000Mτ≤ ≤ (for each fixed point, a trajectory is composed
of 2000 time elements). Once the optimization routine is initialized, a two-dimensional
cubic spline is employed to represent the corresponding state along the tube; interpolation
for states at any point along the tube is then available. The global method, as detailed in
Section 3.5, initially generates a population of 100 uniformly distributed random initial
search vectors, S′
. In this case, only the initial condition with the lowest kinematic
boundary condition error norm, ( )ftψ is retained. The manifold trajectory that is
targeted as a result of this process is highlighted in blue in Figure 4.7. The local, hybrid
direct/indirect routine completed 90 iterations for convergence to an insertion point with
tolerances of 1 x 10-7 on position and velocity.
79
Figure 4.7 Stable Manifold Tube for 12-Day L1 Halo Orbit (Green) and Target Reference Trajectory Along the Manifold (Blue)
The final low-thrust solution appears in Figure 4.8. The solid blue trajectory indicates
periods of engine on-time, and the dotted line represents the fact that the trajectory has
arrived on the stable manifold. The engines are off during an asymptotic approach to the
orbit. During the powered phase, the spacecraft engine is “on” for a total of 25.96 days,
and the remaining translunar coast on the stable manifold is 10.85 days, for a total
transfer time of 36.82 days.
Initial Guess for
Insertion Point
Earth
Moon
80
Figure 4.8 Low-Thrust Short Transfer to a 12-Day L1 Halo Orbit
The trajectory exhibits the “spiral” structure that is commonly observed in low-thrust
applications, as the spacecraft gradually builds up momentum to insert into the target
trajectory. The position and velocity costate histories appear in Figure 4.9. Clearly, an
initial out-of-plane component on the thrust direction is required to enable the planar
Earth
Moon
81
orbit to gradually shift to the inclined insertion point. This out-of-plane component also
oscillates however, and all of the velocity costates increase in magnitude during the
approach. In comparison, the position costates diminish with increasing time. It is also
apparent from Figure 4.9 that the position and velocity costates maintain a relatively
well-behaved and organized structure during the initial spiraling of the powered phase.
But, further along the path, a distinctly nonlinear behavior is clear in the vicinity of the
insertion point. Finally, note that the magnitudes of the costates along the converged
solution are not intuitive, demonstrating the usefulness of the adjoint control
transformation (ACT).
Figure 4.9 Position and Velocity Costate Time Histories for
the 12-Day L1 Halo Orbit Transfer
The performance of the mass costate, χm , appears in Figure 4.10. The spacecraft mass
and thrust histories are also plotted in the figure. The monotonic increase in χm, observed
upon inspecting equation (3.19), is clear in the plot. The spacecraft mass ultimately
reaches 1139.52 kg, for a total fuel mass consumption of 360.47 kg. The thrust profile
maintains an oscillatory structure due to its dependence on the magnitude of the primer
vector in equation (3.36). The thrust magnitude initially peaks at 2.32 N, but equation
82
(3.36) also indicates that, due to the dependence on mass, a decrease occurs as propellant
is gradually expelled, reaching values as low as 0.94 N. These observations are
summarized in Table 4.2.
Figure 4.10 Time History of Propulsion Related Parameters
for the 12-Day L1 Halo Orbit Transfer
Table 4.2 12-Day L1 Halo Orbit Transfer Data Summary
Parameter Value Units
Powered Time 25.965818 day
Coast Time 10.849792 day
Total Transfer Time 36.815610 day
Final Spacecraft Mass 1139.524870 kg
Propellant Mass Consumed 360.4751292 kg
Tmax 2.317819 N
Tmin 0.936037 N
v∆ 3.020521 km/s
83
4.2.1.2 “Long” Transfer
Using the same 12-day L1 halo orbit, a different locally optimal trajectory is also
generated. Incorporating an initial search vector that targets the vicinity of the second
closest approach (in backwards time) results in a “long” transfer scenario, such that a
larger percentage of the transfer time occurs when the vehicle moves on the stable
manifold, e.g., with the engines off. The target point is illustrated in Figure 4.11 along
the same stable manifold tube that previously appeared in Figure 4.7.
Figure 4.11 Stable Manifold Tube for 12-Day L1 Halo (Green) and Initial Target Reference Trajectory Along the Manifold (Blue) for Long Transfer
The final solution for the transfer as plotted in Figure 4.12 reflects the increased time
on the stable manifold. For this “long” transfer, the powered phase lasts 21.53 days and
the coast phase 21.98 days for a total time-of-flight, TOF = 43.5 days. This represents a
decrease of 4.43 over the powered arc when compared to the “short” transfer. But, an
additional 11.13 days is added to the coast time. The total increase in transfer time
compared to the “short” transfer is 6.7 days. Note also that the low-thrust transfer arc
clearly departs from the original spiral structure once the spacecraft reaches the stable
Initial Guess for
Insertion Point
Earth
Moon
84
manifold. The shape of the manifold trajectory also differs significantly from that along
the “short” transfer. Note also that a different manifold trajectory is incorporated
compared to that of the “short” transfer.
Figure 4.12 Low-Thrust Long Transfer to a 12-Day L1 Halo Orbit
Earth Moon
85
The corresponding time histories of the costate variables corresponding to the “long”
transfer appear in Figure 4.13 and Figure 4.14. The position and velocity costates
confirm the oscillatory, nonlinear behavior observed previously. The final spacecraft
mass is 1083.87 kg, yielding a total fuel consumption of 416.13 kg. Thus, even though
the engine is “on” for a shorter duration, the long transfer consumes 55.83 kg more fuel
than the short transfer. Some information that contributes to an understanding of this
mass penalty is apparent in Figure 4.14. As the transfer approaches the periodic orbit, the
engine supplies a thrust level that oscillates with increasing amplitude. In this case, the
thrust magnitude reaches a maximum value of 3.66 N and a minimum level equal to 0.72
N. These thrust magnitudes obviously correspond to lower specific impulse ranges, and
less efficient thrusting. These results are summarized in Table 4.3.
Figure 4.13 Position and Velocity Costate Time Histories
for the 12-Day L1 Halo Orbit Transfer
86
Figure 4.14 Time History of Propulsion Related Parameters
for the 12-Day L1 Halo Orbit Long Transfer
Table 4.3 12-Day L1 Halo Orbit Long Transfer Data Summary
Parameter Value Units
Powered Time 21.982297 day
Coast Time 21.529817 day
Total Transfer Time 43.512114 day
Final Spacecraft Mass 1083.871837 kg
Propellant Mass Consumed 416.128162 kg
Tmax 3.657608 N
Tmin 0.715391 N
v∆ 2.913369 km/s
87
4.2.2 Transfer to a 14-Day L1 Vertical Orbit
A 14-day L1 vertical orbit is selected from Figure 4.2. The associated manifold and the
target transfer trajectory appear in Figure 4.15. The initially targeted insertion point is
over 150,000 km from Earth, that is, approximately 50,000 km further than the insertion
points on the stable manifold corresponding to the 12-day halo orbit. Due to the
maximum z-amplitude of the orbit, Az = 57,000 km, the manifold trajectories are
significantly out-of-plane at several points along the path. A global population of 500
uniformly distributed initial search vectors, S′
, are created to initially propagate a more
“dense” set during the “shotgun” process. The local optimization scheme required 200
iterations to determine a solution. The larger number of iterations is not unexpected since
the thrust profile is required to “lift” the initial state along the planar parking significantly
out-of-plane.
The final transfer arc from the Earth parking orbit to vertical orbit insertion appears in
Figure 4.16. The spacecraft achieves insertion onto the stable manifold after 18.26 days,
and then coasts for 27.81 days. Thus, the total transfer time is 46.06 days. Time histories
of the costates and the propulsion parameters are plotted in Figure 4.17 and Figure 4.18.
Note that thrust amplitudes are bounded between 0.75 N and 2.3 N. This thrust range
ultimately drives the spacecraft mass to a final value of 1128.4 kg; the total mass of fuel
that is consumed is 371.64 kg. Despite the out-of-plane requirement and an insertion
point that is located at a distance farther from the Earth, this transfer still requires less
fuel than the long transfer to the 12-day L1 halo orbit.
88
Figure 4.15 Stable Manifold Tube for 14-Day L1 Vertical Orbit (Green) and Initial Target
Reference Trajectory Along the Manifold (Blue)
Initial Guess for
Insertion Point
Earth
Moon
89
Figure 4.16 Low-Thrust Transfer to a 14-Day L1 Vertical Orbit
Earth
Moon
90
Figure 4.17 Position and Velocity Costate Time Histories
for the 14-Day L1 Vertical Orbit Transfer
Figure 4.18 Time History of Propulsion Related Parameters for the
14-Day L1 Vertical Orbit Transfer
91
Table 4.4 14-Day L1 Vertical Orbit Transfer Data Summary
Parameter Value Units
Powered Time 18.257074 day
Coast Time 27.807328 day
Total Transfer Time 46.064402 day
Final Spacecraft Mass 1128.364331 kg
Propellant Mass Consumed 371.635669 kg
Tmax 2.307188 N
Tmin 0.748454 N
v∆ 3.855672 km/s
4.2.3 Transfer to a 14-Day L2 Butterfly Orbit
The final orbit selected from Figure 4.2 for demonstration of the design of low-thrust
transfers in this problem is a 14-day L2 butterfly orbit. The associated manifold and first
guess for a manifold insertion trajectory appears in Figure 4.19. The initially targeted
insertion state along the stable manifold, like the vertical orbit, is over 150,000 km
(actually 162,000 km) from Earth. This orbit is also unique because the manifold
trajectories exhibit unreliable extreme sensitivities to the angle-like parameter, θM. This
behavior is observed in several L2 orbits as well as orbits with very low stability indices.
When the stable manifolds associated with these orbits pass within the vicinity of the
Earth, the erratic behavior is apparent in Figure 4.19. As a result, the variable θM is fixed
(θM = 8.0 always), that is, the initial condition associated with the target stable manifold
trajectory, ( )s iX t
, does not change. (A lower cost might be achieved by selecting new
values θM and solving the problem again.) A global population of 500 uniformly
distributed initial search vectors, S′
, is established in the “shotgun” phase. From the
global method, a transfer trajectory resembling the “long” transfer seen in Figure 4.12 is
generated.
92
The final converged trajectory appears in Figure 4.20. The spacecraft achieves
insertion into the stable manifold after 32.95 days, and coasts for 49.18 days. Clearly,
this transfer is longer than all other examples, with a total transfer time of 82.13 days.
Figure 4.21 and Figure 4.22 include the time history of the costates and propulsion
parameters. The thrust amplitudes are bounded between the ranges of 0.59N and 1.93 N,
the lowest values in the four examples here. These thrust-ranges ultimately drive the
spacecraft to a final mass of 1166.84 kg, thus yielding a propellant cost of 333.16 kg.
Figure 4.19 Stable Manifold Tube for 14-Day L2 Butterfly Orbit (Green) and Initial
Target Reference Trajectory Along the Manifold (Blue)
Initial Guess for
Insertion Point
Earth Moon
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Figure 4.20 Low-Thrust Transfer to a 14-Day L2 Butterfly Orbit
Earth
Moon
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Figure 4.21 Position and Velocity Costate Time Histories
for the 14-Day L2 Butterfly Orbit Transfer
Figure 4.22 Time History of Propulsion Related Parameters
for the 14-Day L2 Butterfly Orbit Transfer
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Table 4.5 14-Day L2 Butterfly Orbit Transfer Data Summary
Parameter Value Units
Powered Time 32.950829 day
Coast Time 49.175507 day
Total Transfer Time 82.126336 day
Final Spacecraft Mass 1166.835826 kg
Propellant Mass Consumed 333.164174 kg
Tmax 1.928668 N
Tmin 0.596077 N
v∆ 3.226374 km/s
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5. SUMMARY AND RECOMMENDATIONS
5.1 Summary
A strategy for the determination of finite-thrust transfer trajectories that deliver a
spacecraft into collinear libration point orbits is developed. The computational scheme
involves a systematic sequence of steps to generate these transfers. The resulting control
law requires that the engine always operates at maximum power. These transfers can be
decomposed into two phases – a fully powered phase followed by the arrival at a
trajectory along the stable manifold tube, i.e., an un-powered phase yielding an
asymptotic arrival to the vicinity of the target orbit. Clearly, transfers to collinear
libration point orbits are unique due to the availability of the stable manifold that allows
for natural transfer arcs. The manifolds do not always pass conveniently close to the
Earth, however. Low-thrust options serve as a bridge between an Earth parking orbit and
the stable manifold. The initial powered phase, however, requires the determination of an
unknown thrust magnitude and direction history, i.e., the primary controls in the
“steering” problem. Variable specific impulse engines, an advanced prototype low-thrust
engine, allow the thrust profile to vary via throttling of the specific impulse. To resolve
these unknown controls, optimal control theory is used to derive the two-point boundary-
value problem with free final time that corresponds to a necessary and sufficient
condition for a maximal value of final spacecraft mass. Thus, optimal control is useful
not only for determining a control law, but also for obtaining locally minimal transfer
costs.
Solving the hybrid direct/indirect local method via direct shooting requires the
determination of extremely sensitive initial costates, with magnitudes that supply little or
no physical meaning. The adjoint control transformation yields a mapping process to
97
resolve these costates into meaningful quantities such as initial angles, angular rates, and
thrust magnitude. Even though this mapping does not completely remove the numerical
sensitivities, it is used to determine bounds on the initial conditions. Randomized
collections of initial conditions within these bounded regions are then propagated in a
global “shotgun” approach. The terminal errors in the elements of the state vector for
each trajectory, at each instant in time, are then computed for any potential insertion point
along the stable manifold tube, resulting in a “best” initial guess, i.e., the initial search
vector that provides information such as the “starter” state vector.
The initial search vector is then used to initiate a local hybrid direct/indirect
optimization problem. Rather than solve the complete optimal control, two-point
boundary-value problem (an indirect problem), direct shooting is utilized to directly
reduce the performance index. Additional design variables are incorporated into the
search vector, such as the initial departure angle along the parking orbit, and the angle-
like and time-like variables that parameterize the current state along the manifold tube.
Components of the indirect problem, such as parameterizations for the controls,
characterize the nature of the hybrid direct/indirect shooting algorithm. Direct shooting
is accomplished via a medium-scale SQP algorithm, where the only active constraints are
the kinematic boundary conditions.
To demonstrate this methodology for the determination of low-thrust transfers, the
problem of lunar south pole coverage in the Earth-moon problem is introduced. Families
of halo, vertical, and “butterfly” orbits are generated using simple targeting procedures.
The orbits generated in these families meet the specified criteria and comprise potential
orbits in a relay satellite configuration between a facility at the lunar south pole and
Earth-based ground stations. Ultimately, information such as period (used in satellite
phasing), and the stability index (used in estimating potential stationkeeping costs)
determine the orbits that are selected for further analysis. A sample orbit is selected from
each of the different families to validate the low-thrust transfer concept.
98
5.2 Recommendations for Future Work
These transfer trajectories are preliminary in nature. Design within the context of the
circular restricted three-body problem is only an initial step in the design of actual
trajectory arcs in the full ephemeris model. Further development in this problem includes
an extension of the control law and solutions using a higher fidelity gravity model.
Numerical determination of a single transfer trajectory is nontrivial, thus, an exhaustive
trade study is not currently practical. However, this preliminary analysis is a crucial step
towards balancing the lowest possible transfer cost with the lowest possible orbital
maintenance costs for long duration coverage missions. Other, more sophisticated
transfer schemes incorporating multiple manifolds, and potential lunar flybys are not
included at this time. Examination of a wider range of orbits is also likely to yield
alternatives not yet apparent. Future investigations of transfers to these orbits, including
highly elliptical orbits, would also benefit from the mission analysis results of lunar south
pole coverage.
As mentioned previously, computational issues significantly impact the capability to
produce results. Integration time is significant, and hundreds of iterations are necessary,
with typical run-times of 2 or more hours. Developing an algorithm in a programming
language optimized for speed will allow more efficient numerical processes and expand
analysis options.
Finally, a full range of local methods, or more highly sophisticated global methods, to
identify potential solutions were not attempted. Other approaches, mentioned in Chapter
1, that use control parameterizations for multiple shooting, collocation, and dynamic
programming may be useful. While some of these methods may require the
discretization of the continuous trajectory into many impulsive burns, it may still result in
more rapid convergence to a solution. Clearly, the low-thrust problem opens many
avenues for future research efforts in numerous problems, including the lunar south pole
coverage problem.
99
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