The attitude control of fixed-wing MAVS in turbulent environments

Abdulghani Mohamed n, Kevin Massey, Simon Watkins, Reece ClothierSchool of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia

a r t i c l e i n f o

Available online 27 January 2014

Keywords:Micro Air Vehicle (MAV)TurbulenceGust alleviationAttitude controlSensorsGust perturbation process

a b s t r a c t

The small scale and portability of fixed-wing Micro Aerial Vehicles lend them to many uniqueapplications, however their utility is often limited by ineffective attitude control in turbulent environ-ments. The performance of attitude control systems themselves are affected by a variety of factors.Assessment of this system’s performance needs to be viewed in relation to the MAVs’ unique constraints.Certain aspects and limitations of MAV attitude control related issues are addressed in the literature, butto fully address the degradation of utility, the entire system must be examined. These issues can only befully addressed when considering them concurrently. There is no framework for defining the attitudecontrol problem explicitly for MAVs. This paper attempts to (1) Define the MAV attitude control problemwith respect to the unique constraints imposed by this class of Unmanned Aircraft; (2) Review currentdesign trends of MAVs with respect to vulnerability to atmospheric turbulence.

& 2013 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372. Defining the MAV attitude control problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.1. Nature of MAVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392.2. Turbulence characteristics and MAV scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.2.1. Low Reynolds number effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412.3. Effect of size on stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.3.1. Roll rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432.3.2. Actuation time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432.3.3. Mass and payload restrictions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3. Potential solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.1. Passive approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.2. Active attitude control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.2.1. Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

1. Introduction

Micro Air Vehicles (MAVs) are man-portable Unmanned AirVehicles (UAVs) and their size lends them to low altitude, close-insupport operations. Low altitude flights pose a challenging opera-tional environment for MAVs. One particular challenge is ensuring

sufficient attitude control in the presence of significant turbulence.The loss of directional and attitude control can be due to rapidchanges in lift on the wings or the simple fact that the MAV iscarried along with local currents in the air. Previous work hasdemonstrated the inability of an MAV to reach its pre-programmedwaypoints in the presence of a steady 4.6 m/s magnitude wind asillustrated in Fig. 1 [37]. This degradation in performance puts theaircraft at risk and often leaves the MAV unable to complete itsmission [65]. However, turbulence poses even a greater threat tothe vehicle’s attitude stability [16,17,41,47]. Ol et al. [35] identifiedseveral challenges associated with MAVs operating in complex

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Progress in Aerospace Sciences

0376-0421/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.paerosci.2013.12.003

n Corresponding author. Tel.: +613 992 562 42.E-mail addresses: abdulghani.mohamed@rmit.edu.au (A. Mohamed),

kevin.massey@rmit.edu.au (K. Massey), simon.watkins@rmit.edu.au (S. Watkins),reece.clothier@rmit.edu.au (R. Clothier).

Progress in Aerospace Sciences 66 (2014) 37–48

environments. The challenges are represented below within thecontext of this paper.

� Reduce path divergence for confined flights between buildings,or in cluttered/complex environments.

� Remain controllable in ground effect or in the local wake ofobstacles.

� Suppress turbulence while keeping sensors on target.� Exploit low-resolution sensors, and minimise computational

resources.� Use minimal on-board energy storage.� Attain sufficient control power to manoeuvre.� Remain agile to perform demanding manoeuvres.

On-board autonomous control systems are required to ensure thesafe and controlled operation of MAVs in very turbulent environ-ments. The design of the attitude control system, including sensors,needs to address the unique constraints of MAVs. Various groups ofresearchers have addressed the attitude control problem from oneperspective only, where many of their published work only addresseslow level detail (i.e. specific issues related to sensory performance, orcontrol algorithms) without relating back to the hierarchical struc-ture of the problem. A robust solution to the attitude control problemof MAVs can only be realized when the attitude control problem isproperly defined. This definition involves considering all the relatedissues concurrently and relating them to the MAVs operationalenvironment and physical constraints. This paper therefore

endeavours to create a framework for the attitude control problemof MAVs by exploring most of the factors involved. The authors areunaware of any published work which sets the framework indefinition of the attitude control problem of MAVs.

2. Defining the MAV attitude control problem

MAV’s size with respect to turbulence, operational environ-ment, and mission requirements can have detrimental effects ontheir attitude stability. These features also bring greater controlchallenges compared to larger aircraft, which operate in a differentenvironment, and flight regime. Larger aircraft are designed tooperate in relatively smooth flow at higher Reynold’s numbers.Their large mass and inertia attenuates the effects of the higherturbulence frequencies, thus reducing the control bandwidth. Thephysical design and operational requirements of MAVs signifi-cantly vary from larger aircraft. MAVs require unique agility/manoeuvrability to operate in complex terrain. Avionics develop-ment fulfilled the constraints and requirements of larger aircraft,which benefit from larger payload mass and volume. MAVs didbenefit from the introduction of Micro-Electro-Mechanical Sys-tems (MEMS) technology which enabled miniaturization of itsavionics, however the utility of MAVs require ever smaller andfaster avionics with the lowest power consumption.

The chart illustrated in Fig. 2 outlines the unique constraints ofMAVs and its adverse effects on attitude control. The following

Nomenclature

b wing spanCL lift coefficientClp roll damping derivative in rollIxx moment of inertiaIu turbulence intensity percentageLp roll dampingq dynamic pressureS wing areatp aerodynamic advection time-lag of the turbulent

properties over the wingtd time-lag associated with the detection of the distur-

bance (i.e. sensor time-lag)tm time-lag associated with the communication between

the sensor and the controller

tc microcontroller’s processing time-lagts servo/actuator response time-lagta pressure distribution lag following deflection of an

aerodynamic surfaceti inertial response time-lag following the aerodynamic

variation caused by actuationtv displacement lag of the wing through a fluid, rather

than a vacuumtCR total time-lag associated with a controlled responseU0 fluctuating component of velocityU velocityŪ mean velocity componentm massg gravitational accelerationτr time constant

Fig. 1. MAV flight path through a simulated urban environment: (a) without wind (b) with 4.6 m/s steady wind [37].

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–4838

subsections will explore some of these constraints in detail. Fig. 2represents an overview of the unique constraints of MAVs whichhave detrimental effects on attitude stability.

2.1. Nature of MAVs

Mueller [33] categorizes MAVs according to their wingspan andmass, which are defining parameters as they affect their transport-ability and in turn, their utility. MAVs are further defined by themilitary as being smaller than Tier 1 Unmanned Aerial System (UAS),which are represented by the RQ-11 Raven, Dragoneye or similar [40].Conventional fixed-wing MAVs are further characterized by lowerflight speeds, which varies with wing loading. According to Eq. (1),which describes the relationship of wing-loading and velocity; wing-span reduction translates to higher flight velocities. It should be notedthat Intelligence, Surveillance, and Reconnaissance (ISR) missions atlow altitudes become easier with lower velocities due to limitations incamera frame rates, data transmission rates, and motion blur.

Up

ffiffiffiffiffiffiffimgS

r: ð1Þ

Nano Aerial Vehicles (NAVs), MAVs, and Mini-UAVs (MUAVs)can be compared with flying insects and birds. More recentlyresearchers have focussed their efforts on bio-inspired MAVdesigns, hoping to improve MAV performance through observa-tions of the natural evolutionary process. Examples of bio-inspireddesigns include the Festo SmartBird, and the AerovironmentHummingbird. To develop an appreciation of how MAVs comparewith nature0s flyers, the plot of Fig. 3 was generated. This plotshows the relationship between wingspan and velocity for a rangeof birds, insects, and fixed-wing aerial vehicles. The presentedinsect velocities are mean flight speeds [23,57], while the birds’velocities represents their cruise speed [53]. The fixed-wing aerialvehicles, mostly consisting of MAVs, are illustrated as a line ratherthan discrete points showing a velocity range, as reported by theircorresponding manufacturers. Flapping insects are more relevantto miniature ornithopters, while soaring and gliding birds aremore relevant to fixed-wing MAVs. It is clear from this plot thatalthough MAVs might somehow coincide with birds, they seem tobe biased towards higher velocities owing to their propulsionsystem and higher wing loading. However, in the lower speed andspan domains, there is more correlation between birds and MAVs.

2.2. Turbulence characteristics and MAV scales

MAVs operate exclusively within the atmospheric boundarylayer, which typically extends up to 5 km above ground leveldepending on surface heating, climatic conditions, and terrain [18].There is a significant flight challenge posed by the atmosphericboundary layer where the turbulence intensity increases rapidly atlower altitudes closer to the ground. The flow in that region isdominated by horizontal transport of atmospheric properties.As air travels over buildings and various obstacles, there will bea local increase in wind speed due to favourable pressure gradientsin addition to significant vortex shedding [63]. Mean wind speedsabove a certain threshold can detrimentally affect MAV stability.However, turbulence (i.e. wake from obstacles induced by wind)poses even greater hazard. Turbulence intensity is defined as thefluctuating component of velocity with respect to the meanvelocity component (see Eq. (2)). Walshe [58] described thevariation of turbulence intensity with height and terrain, whereit as shown that intensities can reach 415% at low altitude insuburban environments. Roth [43] provided a comprehensivereview of turbulence in cities. It was shown that cities can attainturbulence intensities 440% within 10’s of meters above theground.

Iu ¼ffiffiffiffiffiffiffiffiffiffiffiðU0Þ2U

sð2Þ

Another important factor in quantifying turbulence is thelength scale, which is characterized by the average dimensionsof the larger eddies within the flow. The frequencies of variouslength scales and their magnitudes evident in the atmosphere canbe depicted as shown in the power spectral density (PSD) plot ofFig. 4. In this plot, MAV destabilizing gusts correspond to flows onthe rightmost side of the plot, while other flows are consideredquasi-static relative to the flight of MAVs.

The flight regime, turbulence scales, frequencies, and intensi-ties relevant to MAVs has been studied by a number of researchers[1,33,34,42,60,64]. Although the atmospheric boundary layer iswell documented and properly understood through stationarymeasurements for various purposes, these measurements do notrepresent vehicles travelling through it at relative speeds. Watkinset al. [61] presented one of the first attempts at characterizing the

Miniature Size

Environmental Challenges

Size Weight and Power Limitations

Non Scalable Phenomena

Non-Linear Flow Phenomena

Low Reynolds Number Flows

Poor Avionics Performance

Poor Attitude ControlStorage

Limitations

Attitude Instabilities

Operational Requirements

Cost Requirements

Slow & Low Level Flying Higher Relative Turbulence

Clouds, Humidity, Rain, Heat & Sunlight

Vibration, Routine Impacts

& Noise

MAV Unique Constraints Side-Effects on MAV Attitude Control

Low Mass Low Inertia Higher Perturbation Rates

Slow ActuationTechnological

Limitations

Fig. 2. MAV’s unique constraints and its influence on attitude control.

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–48 39

dynamic effects of turbulence as MAVs travel through it. This studyexplores the relationship between turbulence and relative vehiclespeed. The data was acquired using four multi-hole pressureprobes laterally separated on a mast above a test car. Thepresented results are particularly useful in providing an under-standing of the turbulence characteristics experienced by a mov-ing vehicle. As expected the relative turbulence intensities (i.e.from the perspective of the moving vehicle) were found to reduceas a vehicle’s travelling velocity increased (see Fig. 5). Further-more, the flow’s pitch rate variations were found to be significant.Fig. 6a illustrates these variations where the pitch angle recordedfrom four probes laterally separated by 150 mm (during a 2 ssampling time) shows large fluctuations in the order of 7101.Although it might seem that there is correlation between the four

probes, closer examination reveals that there are differences,where at some instances the variation is �151 (see Fig. 6b).Fig. 7a shows the pitch angle variation at various lateral spacings.Lateral spacing can be of particular interest for understandingwingspan effects of turbulence on MAVs. The latter is clearlyillustrated in Fig. 7b, which shows the pitch angle coherence atvarious turbulence length scales for different lateral spacings. Thedisplayed results show that pitch angle coherence is dramaticallyreduced with reducing turbulence scale. The increase of lateralspacing also results in reduced coherence.

Fig. 4. Power spectral density plot of velocity fluctuations within the atmospherereproduced from [56].

Fig. 5. The relationship between turbulence and velocity [61].

Fig. 3. Velocity vs. span for a multitude of flying animals and robots.

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–4840

Furthermore, the small turbulent length scales (equivalent toMAV chord lengths and spans) and frequencies (0–50 Hz) werefound to have maximal instability effects. Intuitively, the responseof fixed-wing MAVs to these gust inputs depends on the stabilityof a particular axis. MAVs tend to inherit higher sensitivity to gustinputs in the rolling and pitching axis in contrast to the yawingaxis. This is traced back to the physical design of the fixed-wingvehicle, where it is directionally stable in the presence of a largermoment arm between an aerodynamic surface, associated withcontrolling a particular axis, and the Centre of Gravity (CG). As agust imparts on a wing’s leading edge, the flow angle and velocityis altered, inducing variations in the pressure distribution. If thegust front were two-dimensionally symmetric (i.e. a 2-D wavemotion), then this would result in a purely pitching motion, whichis relieved as the gust passes over the chord. Asymmetric frontswill lead to uneven lift distribution over the wings, inducing arolling motion. Since turbulence structures are highly threedimensional in nature, instability to rolling motion forms the mostsignificant disturbing factor for MAVs. Significantly large turbu-lence structures can be considered quasi-static, and easily com-pensated for, when compared with smaller eddy structures.Thompson and Watkins [54] confirm that instability to rollingmotion forms the most significant disturbing factor for small fixedwing craft flying outdoors. As smaller wing structures are oftenrigid, the MAV will tend to "Heave" at length scales equivalent orgreater than its wingspan. Heave can also be hazardous, requiring

complicated control to mitigate. Furthermore, the aerodynamics ofthe airflow over the structure can also differ significantly underheave than that expected at higher Reynolds Numbers [32]. Heaveand roll perturbations are highly dependent on the turbulencelength scale, where smaller scales will result in lower coherence.Both need to be considered for MAVs flying in turbulence.

2.2.1. Low Reynolds number effectsIn addition to turbulence, there are also other significant phenom-

ena that contribute to the vehicle’s attitude instability such as theformation of Laminar Separation Bubbles (LSBs) and vortical cores.These non-linear flow phenomena occur below a Reynolds Number of200,000 which is typical for MAV operation. The LSB’s structure insmooth flow and behaviour has been studied as early as the 1960sthrough computational and experimental means [8,9,19,21,36,39]. Themajority of research in this area relies heavily on empirical windtunnel data rather than conventional analytics or large-scale data [55],since the majority of lift is influenced by tip vortices [32]. Theaerodynamic performance in this domain remains poorly understoodeven though there is considerable research in this area. Historically,the work conducted on low-Reynolds flows was limited by theinstrumentation available at the time. Ravi [42] explains that the useof modern sensitive equipment enabled measurements of force orpressure without influencing the developing flow giving confidence inresults. Historic measurement equipment and techniques interfered

Fig. 6. Pitch angle variation: (a) 2 second sample (b) 0.2 second sample [61].

Fig. 7. (a) Pitch variation fluctuation versus measurement spacing (b) pitch angle coherence [61].

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–48 41

with the flow and prevented measurements close to the airfoil surface.The latter explains why some discrepancies in results where noted inthe historic published data.

The influence of turbulence on airfoils has often been studied atsignificantly higher Reynold’s numbers than those experienced byMAVs [14,22,48,50,51]. However, there are more relevant studiesinvestigating airfoil performance at Reynolds numbers relevant toMAVs [12,20,25–29,42,45,62].

Ravi [42] studied the influence of turbulence on airfoil perfor-mance in low Reynolds flows. It was shown that in low turbulence(i.e. nominally smooth flow) laminar flow was evident over aportion of the chord, and in the presence of adverse pressuregradient, flow separates. Depending on a number of factors eitherreattachment occurs further downstream, forming a LSB, orremain detached while transitioning, hence inducing a turbulentboundary layer. Fig. 8 shows the anatomy of an LSB forming over asurface [21]. LSBs are a significant contributor to the variability inairfoil performance in low Reynolds number flows. LSBs can alsohave detrimental effects on MAV stability, inducing severe motionperturbations and/or loss of lift. However, in elevated turbulencelevels the flow behaviour is significantly different. Increasingturbulence intensity results in increased magnitude of perturba-tions in addition to shortening of the LSB’s length.

When a wing encounters a sudden large pitch angle variation(i.e. gust), separation of the shear layer occurs near the leadingedge. Unlike smooth flow, the shear layer transitions earlier due toambient disturbances and roll-up. This roll-up leads to the forma-

tion of vortical cores. Vortical cores have higher flow circulationand attain rapidly growing/dispersing pulsation cycles. LSBs incontrast attain less shear layer fluid entrainment and circulation.Ravi [42] asserts that no rapid growth or pulsation patterns werenoticed in LSBs. Additionally, LSBs and vortical cores both inducepseudo-camber effects hence influencing the coefficient of lift ofthe airfoil, CL. This effect has greater influence in smooth condi-tions since there is stronger suction inside a LSB compared to avortical core, which in turn induces pitching or rolling oscillationsthat adversely affect MAV performance. Turbulence has greaterinfluence over the suction side of the airfoil. Increasing turbulenceintensity results in greater resistance to separation, thereforeresulting in a delayed stall, reduction of the lift-curve-slope, andan increase of CL max. However, increasing turbulent length scalefor a given turbulence intensity results in an increase of the lift-curve-slope and CL max (see Fig. 9). Turbulent flow generally resultsin higher lift at AoA in the stall region while an opposite trend isevident in smooth flow. Turbulence can therefore enhance anairfoil performance at low Reynolds numbers, preventing the useof flow control devices to trip early turbulent transition.

2.3. Effect of size on stability

The reduced wingspan of MAVs and low inertial of the vehicle hasmany operational benefits, however it can contribute to degrading theattitude control performance. The following subsections explore someof the side effects of miniaturization on attitude stability.

Turbulence Intensity

CL

α

CL

α

Length Scale

Fig. 9. Airfoil aerodynamic performance variation with increasing: (a) turbulence intensity (b) length scale.

Fig. 8. Anatomy of laminar separation bubbles, reproduced from [21].

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–4842

2.3.1. Roll ratesClassical fixed wing aircraft stability analysis provides a method

whereby the relative stability of aircraft can be compared as afunction of size. The roll damping stability derivative provides anindication of the degree to which an aircraft is susceptible to anundesired roll. Roll damping is typically denoted by Lp and isshown as follows:

Lp ¼qSb2Clp

2IxxUð3Þ

Under some simplification, the roll mode time constant can beapproximated as the inverse of Lp as follows:

τr ¼�1Lp

ð4Þ

τr �ffiffiffib

pð5Þ

Assuming that the primary factor for scaling aircraft is thewingspan, b, and that the remaining factors also scale withwingspan as shown in Table 1, then using Eq. (5), it can be shownthat the time constant is proportional to the square root of thespan. Note that the proportionality of the velocity to wingspan wasderived based on the cruise velocity of a number of fixed wingpropeller aircraft as shown in Fig. 10, and that the power of 0.5 isreasonable from the range of spans from 0.4 to 40 m (e.g. Wasp toC-130), though there is more spread as the size of the aircraft isdecreased. It is also worth mentioning that for the typical tube andwing aircraft configuration there is little that can be done toimprove the situation. The single biggest problem is that of themoment of inertia, but to significantly affect the moment of inertiarequires either increasing the mass which leads to a biggerwingspan or shifting the mass away from the longitudinal axisof the aircraft.

That the roll time constant decreases as the size of the aircraftdecreases is intuitive, but it is instructive to note that it decreaseswith the square root of the span. Thus if one were to make a1/100th scale model of a C-130 with a 0.4 m wingspan, one wouldexpect the time constant to be on the order of 0.1 s (versus 1 s forthe C-130, under the assumption that the C-130 has acceptable rolltime constants). Handling qualities for aircraft have been char-acterized in numerous studies and most people reference MIL-F-8785C which specifies ranges for most flying qualities. It isinteresting to note that the limit on roll rate is provided only as an

upper limit in the MIL-spec, as it is must have been assumed thatpilots are always happier with increased roll or that is notphysically possible to get unreasonably low roll time constants(which is true for manned aircraft). But for small SUAS and MAVslow roll time constants are an issue.

The low roll time constants lead to a number of issues. Whenthere is a human-in-the-loop, the time constants can be so shortthat a human cannot react fast enough. Low time constants alsolead to pilot fatigue. For human-in-the-loop control of MAVs, evenlonger time constants are required due to the additional lag addedby a communications links.

The problem is not simply solved through the addition of anon-board inertial based autopilot for two reasons. In the mostturbulent cases, the roll control system consisting of typical on-board rate sensors, the autopilot, and the aileron servos combineto be too slow to counteract the roll induced by gusts. In fact, onecan see that for the smallest MAVs typical servos used to deflectailerons may not have a fast enough time constant.

An illustrative example is that provided by Bogos and Stroe [7].The study explored the handling characteristics of a 2 m wingspanmodel, which was a 1/10 scale model of an actual aircraft.As shown in Fig. 11 the time to half for a roll perturbation forthe scale model is reduced from the full-scale value of 0.22 s to0.07 s for the 1/10 scale aircraft, following the square root lawderived above. What is even more important to consider is the rollresponse to a sharp gust as shown in Fig. 12. Here it is seen thatthe frequency of the response for the scale model is increased by3 (�square root of 10) and perhaps more importantly theamplitude of the roll rate is increased by the same ratio. Revisitingour 0.4 m span, 1/100 scale C-130, one would expect the frequencyof the response to be 10 times higher and the roll rate to also be 10times higher. These examples clearly illustrate the challengesassociated with MAVs with respect to roll response.

2.3.2. Actuation timeThe miniaturization of DC motors enabled the fabrication of

micro-scale servo actuators. As their size is reduced, servos’response characteristics become important. Miniaturization ofservos enabled micro-flight, however their current actuationspeed are insufficient for rapid actuation of aerodynamics surfaces.A servo’s speed depends on the inertial requirements needed toaccelerate the sub-components up to the required speed. Thehigher the torque required to accelerate the subcomponents theslower the response will be and vice-versa. This is substantiallyalleviated in smaller sized servos which require less torque toaccelerate the smaller components, implying faster responses.

Fig. 11. Characteristics transient roll mode [7].

y = 16.862xR² = 0.906

5

50

500

0.1 1 10 100

Flig

ht S

peed

[m/s

]

Wingspan [m]

Fig. 10. Flight speed vs. wingspan for propeller driven aircraft.

Table 1Dimensional analysis of terms affecting roll damping.

Proportionality constant

Span (b) 1Wing area (S) b2

Moment of inertia (Ixx) b5

Velocity (U) b0.5

Dynamic pressure (q) b

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–48 43

Additionally, the slow flight of MAVs implies lower torque require-ments needed to actuate the aerodynamic surfaces, however thehigher control rates required can still be demanding.

One method to improve servo speed (which can be expressed asa maximum frequency) and torque is to replace conventional DCmotors with coreless or brushless motors which provide morespeed and torque. The utility of digital servos can also improvethe maximum frequency, settling time, overshoot and torque.Digital servos are similar to their analog counterparts except thatthey are controlled differently by the servo controller. Digital servosenable higher command rates from the controller, enabling max-imum torque at the beginning of movements. This implies fasterand stronger responses at smaller movements, empowering theattitude control system. The downside of digital servos however isthe higher power consumption. A range of commercially availabledigital servos (o30 g) were surveyed where their "no-load" per-formance is plotted in Fig. 13. The illustrated data was obtained frommanufacturer datasheets. Obviously the performance will degradein the presence of loads. Degradation will depend on flight speed,gust amplitude, and aerodynamic surface area. More research isthus required to identify whether current servo settling times aresufficient to aid rapid actuation or if faster servos are required.

2.3.3. Mass and payload restrictionsApart from range and endurance degradation as a consequence

of size reduction, MAVs also have limited payload and MaximumTake-off Weight (MTOW). In many cases, this prevents the incor-poration of avionics cooling components, vibration dampers, and

stabilized gimbals. The lack of these auxiliary components con-tributes to the degradation of mission performance. Stabilizationgimbals in particular can improve mission effectiveness, sincedeviation from a set flight trajectory can easily occur fromturbulence especially in built up areas where MAVs are most likelyto conduct ISR missions. Gust perturbations can easily blur a videostream or even move targets outside the camera’s field of view.The operation of optical payload sensors require a high degree ofstability for focusing on ground targets. The stringent payloadlimitations of MAVs (�20% of the vehicle’s weight [38]) canchallenge employment of stabilization gimbals. Currently available2-axis and 3-axis gimbals will use most of the payload margin inaddition to a large volume of the fuselage. The problem originatesfrom the actuator’s size and mass, in addition to the volumerequired to translate or revolve the optical sensors. Gimbals areused mainly to improve situational awareness providing a largemechanical field of view. Consequently MAVs often employ 1-axisand 2 axis gimbals. However effective operation ideally requires3-axis stabilization, which is not always an option.

3. Potential solutions

A range of methods have been developed to mitigate thedisrupting effects of turbulence on aircraft stability. These meth-ods are classified as being either active or passive (see Fig. 14).Passive methods involve design considerations that ensure theaircraft has an inherent tolerance to perturbations. For example,adding wing dihedral for lateral stability or the use of flexible

0

5

10

15

20

25

30

0 0.01 0.02 0.03

Max

imum

Fre

quen

cy [H

z]

Mass [Kg]

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.01 0.02 0.03

Tor

que

[N.m

]

Mass[Kg]

Fig. 13. Servo properties under 30 g: (a) max frequency and (b) torque.

Fig. 12. Roll response at a lateral sharp gust for: (a) scale mockup (b) full aircraft [7].

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–4844

membrane wings that can alleviate the wing loads and minimisetransfer of perturbations to the vehicle’s centre of gravity. Activeapproaches involve an electronic system that can sense or predictperturbations or their secondary effects on the MAV. Activeapproaches can be designed to either warn the operator or activelysuppress the induced motion. Turbulence warning systems aresuited for human-in-the-loop operations (for example, windshearwarning systems used in large passenger commercial aircraft).

Systems that actively attempt to counter perturbations causedby turbulence use inner-loop control, where perturbations arecontrolled with respect to the aircraft’s reference frame using flowcontrol techniques to mitigate the perturbations. This can be assimple as deflecting an aerodynamic surface(s) to counteract theresponse. If the reference frame is within the vehicle itself, then itis described as "relative" active attitude control, however when itis external to the vehicle, it is classified as "absolute" activeattitude control. It is important to note that relative attitudecontrol systems require a known starting position or be suppliedwith external information. This limitation can be traced back tothe technological limitations of the employed transducers whichsuffer from measurement drift.

3.1. Passive approaches

Vulnerability to gust-induced instabilities varies with thevehicle0s physical design. There are a number of sizing considera-tions, which can be implemented to the MAVs wings, fuselage, andpropulsion design to attain a stable vehicle in the presence ofdisturbances. Abdulrahim et al. [1] conducted experiments explor-ing the dynamic sensitivity of a model aircraft to atmosphericturbulence by varying a range of parametric factors. The factorsincluded: mass, CG, moment of inertia, wing-loading, and wing-span. The response of the MAV to turbulence disturbances forvariations in these factors is summarized in Fig. 15. It wasgenerally deduced that turbulence-induced perturbations areattenuated with increasing aircraft mass, inertia, and wingspan.However, increasing the vehicle0s inertia reduces the control inputresponse. Increasing wingspan reduces yaw stabilizing influence ofthe wing in addition to reduced pitch and roll damping authority.

Using these passive techniques a highly stable vehicle can becreated. However it is important to realise that an excessivelystable MAV lacks the manoeuvrability to navigate its obstacle-richenvironment. More stability means reduced control authority andvice versa. Passive attitude control is permanent with no "off"switch allowing aggressive manoeuvres when required.

3.2. Active attitude control

Close-in interaction of MAVs is encouraged in ISR missions,where small size, slow flight, and GPS-denied navigation isnecessary [31]. MAVs must therefore be fully autonomous, havingsufficient on-board intelligence to conduct useful missions in thedynamic, unstructured, and random nature of outdoor environ-ments. Autonomous control consists of inner-loop control andouter-loop control. The latter involves the ability to changedirection, altitude and speed, while inner-loop control involvesmaintaining self-stable attitude in the presence of turbulence [32].MAVs require high control input rates, equivalent to or faster thanthe rates that occur in these turbulent conditions. Wang et al. [59]state that the attitude data update rate of an MAV needs to begreater than 25 Hz. Since humans can only achieve up to a few Hz,employment of an active attitude control system is therefore vital.A micro-controller employed by the attitude control system can beprogrammed to provide higher input rates to match the MAV’sattitude data update rate. This can be achieved by employingattitude sensors that allow detection of various attitude relatedparameters for suppressing the response motion. MAV stabilityperformance is thus coupled with the performance of the activeattitude control system.

3.2.1. SensorsCommon to all active turbulence approaches are transducers

able to detect the presence or effects of turbulence. Generallycontrol systems are hindered by their sensing capabilities wherevarious factors involved can limit robust attitude control inturbulent environments (Fig. 16). Many of the factors illustratedshould be addressed in the design of the sensor. These factors needto be considered concurrently when assessing and selecting anoptimum attitude control sensor for MAVs. One of the main factorsrequiring special attention, is the natural physical sequencing ofthe measured parameter in the Gust Perturbation Process. Thisfactor is strongly coupled with a sensor0s detection time-lag, and isdetailed in the following subsection.

The published literature on active attitude control sensors hasfocused on the use of conventional inertial and electro-opticalsensors [2,10,11,15,24,30,44,46,49,52,66]. The apparent objectiveof these studies is the extraction of further performance throughadvancements in signal processing, measurement techniques, and

Wing SpanIncrease

Wing AreaIncrease

Wing-LoadingReduction

Parametric Changes Improving MAV Stability in Turbulence

Mass & inertiaIncrease

Reduced Rolldisturbance Sensitivity &

control Authority

AttenuatePerturbations

Improved Control InputResponse

Improved Pitch DampingAuthority

Reduced Yaw Stability &Control Authority

Can be accounted for by usinglarger rudder area

Requires less rapid but largemagnitude surface deflections

AttenuatePerturbations

(excluding pitch)

Fig. 15. MAV design considerations and their effect on stability.

Aircraft Stability

Passive Approaches

Aircraft Physical Design Considerations

Wing Empennage Propulsion

Active Approaches (Absolute & Relative)

Sense & WarnSystems Control Systems

Human-in-the-loopOperations

AutonomousOperations

Fig. 14. Approaches for stability of unmanned aircraft.

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–48 45

fabrication. Conventional sensors are well-suited for attitude control,thus there is little motivation by the research community to exploreother approaches. Although this perception is somewhat true forlarger UAVs and commercial aircraft, it is not the only possibleturbulence mitigation technique applicable for MAVs. Improvedstabilization will make MAVs a more attractive option for specialistmissions, which are currently dominated by human-in-the-loopvehicles for safety concerns.

3.2.1.1. Gust perturbation process. There are various latenciesunderlying the controlled response of an MAV counteracting a pertur-bation. The latencies can be the result of physical phenomenaor technological limitation. Minimising these latencies is crucialin providing sufficient turbulence motion mitigation. However, tocharacterise and quantify the latencies it is crucial to track andunderstand the process underlying the source of the disturbance(i.e. gusts advection over the wing).

The flow chart in Fig. 17 represents the stick fixed response in rolland was created to illustrate the sequence of events induced by an

oncoming gust (i.e. the gust perturbation process). A perturbation in rollwas chosen since it was previously identified as the most problematicfor MAVs. Between each event there is a theoretical phase-lag. Hence,sensors can be put into context and categorized according to theirmeasured parameter. Theoretically some of the events presented canoccur in the same instance, such as roll acceleration and roll rate, whilethere is a delay between others such as angle of attack and structuralstress. Hence, the sequence illustrated is flow-influenced as well assensor-influenced. The latter statement can be further clarified by theplots of Fig. 18, which illustrate an example of a roll with constantlyincreasing roll rate, comparing some responses of typical sensors. It isclear that accelerometers can capture infinitesimal perturbations whileother sensors are registering no-change.

Acc e

lera

tion

Time1s

Rat

e

Time1s

Dis

plac

emen

tTime1s

Accelerometers

Gyroscopes

Tilt SensorsHorizon Sensors

Optical FlowMulti antenna GPS

Fig. 18. Comparison of various sensor response to a roll with constantly increasingroll rate.

Sequencing ofMeasured parameter MAV Attitude

Control Sensor

Relevant turbulencescales, frequency &

Intensity

Frequency Response

ResonanceFrequency

Precision &AccuracyDrift

Coverage/Availability

Signal conditioning/ComputationalRequirements

Output Time Delay

Detection Time

SWAP requirements

Fig. 16. Considerations associated with assessment of attitude control sensors.

Fig. 17. The sequence of events induced after encountering a gust.

A. Mohamed et al. / Progress in Aerospace Sciences 66 (2014) 37–4846

The nature of the event-related parameter measured by thesensors, and its sequencing in the aforementioned process, willinfluence time-delays. It is important to note that the presentedconventional sensors require the MAV to respond inertially beforea motion perturbation is detected. It is preferable that sensorsdetect disturbances relatively quickly, especially before the vehiclestarts to respond or before they occur. This requirement shouldspark investigations into novel sensory approaches that exploit thefirst four events illustrated in Fig. 17. There is an increasing interestin exploiting flow sensors, which can detect flow disturbances[3–6,13,28,42].

The gust perturbation process0 relationship to the otherresponses in a roll event and correction is shown in Eq. (6). Thisexpression shows the contribution of physical phenomena andaircraft subcomponents to the delayed response to a disturbance.It must therefore be realised that the time-delays stated on sensordatasheets need to be viewed relative to other sources of delay.

Control Response tCR ¼ Gust Perturbation Process tP þ Detection td

þ Multiplexing tm þ Computation tc þ Actuation ts

þ Aerodynamic ta þ Inertial ti þ Virtual Mass tv

ð6Þ

The presented terms can be approximated to determine anominal tCR. The time required to communicate and compute data,tm and tc, by a microcontroller is considered negligible. Further-more, the work presented by Abdulrahim et al. [1], aids theestimation of ta, ti, tv combined. From the presented flightmeasurement results, the response time following actuationis�2 Hz (0.5 s) for a 1 m wingspan MAV flying at 10 m/s. Theresponse time would be expected to scale with the wingspan ofthe MAV as per Eq. (5). Considering the fastest and lightestsurveyed servo actuators, a loaded servo will attain a response of�0.015 s. These estimates lead to the simplifications presented inEqs. (7) and (8). It can therefore be seen that within currenttechnological constraints an MAV of the latter size and speed, willattain a control phase lag of 40.52 s. Based on these assumptions,it would be impractical to reject adverse roll frequencies above2 Hz assuming that the gust perturbation process and the detec-tion process were infinitesimally. If one assumed that the gustperturbation process was on the same order as the response time,then the control response would be on the order of 1 second, andthus MAVs would be unable to compensate for high intensityturbulence at frequencies above 1 Hz.

tCR ¼ tPþ tdþ0þ0þ0:015þ0:5 ð7Þ

tCR ¼ tPþ tdþ0:515 ð8Þ

The above analysis assumes an inertial based detection whichrequires a response (roll) of the MAV before any corrective actioncan be taken. If a sensing or detection system can be developedwhich would provide a means to sense a disturbance before aninertial response was initiated, then the possibility may exist toshorten the overall response time and expand the operationalenvelope of the MAV.

4. Conclusion

Various constraints imposed on MAVs are traced to theirminiature scale. These unique constraints have been identifiedalong with their exacerbating effects on the vehicle’s attitudestability and control. MAV’s turbulent operational environmentwas identified as a major contributor to attitude instabilitieswhere turbulence relevant to MAVs was identified. Gust-induced

roll perturbations where found to be the most problematic.Additionally, the gust perturbation process was explored toillustrate the phase-lag of various gust induced events. The entiresystem’s response time to a gust was explored to stress theimportance of minimizing latencies for effective attitude control.Active and passive methods for MAV attitude control were subse-quently explored, where the importance of active attitude controlsystems for MAV autonomy was discussed. Sensors employed byactive attitude control systems were classified according to thegust perturbation process. Passive methods were also exploredand the trade-off between manoeuvrability and stability wasexplained. This paper established the attitude control problem ofMAVs as a hierarchical problem which needs to be consideredentirely if an effective solution is to be found. It is the intention ofthe authors to provide a better understanding of the issues involvedwith attitude control aiding the formulation of an effective solution.

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