Post on 25-Jan-2017
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DEVELOPMENT OF NEUROFUZZY CONTROL SYSTEM FOR THE GUIDANCE OF AIR TO AIR MISSILES
byEng. Ahmed Momtaz Hosny
Under the Supervision of
Prof. Galal Hassan Professor of Systems Dynamics
Dr. Yasser ZeyadaAssistant Professor of Systems Dynamics
Mechanical Design and Production Department
Neural Network Controller
Fuzzy Logic Controller
Comparison between FLC and NNC
Low Pass Filter
Comparison between FLC and PDC
CONTROL STRATEGY
HOME
Fig. (1.1) Henschel Hs 293 air-to-ship, wireless guided, gliding bomb, model A
Fig. (1.3) The Ruhrstahl SD 1400 'Fritz X' air-to-ship, wireless guided, gliding bomb
Fig. (1.4) Restored Ruhrstahl X-4 Air-to-Air missile
WARHEAD - 1000 LB. - W80 250 KILOTON THERMONUCLEAR OR 1000 LB. CONVENTIONAL HIGH EXPLOSIVE/FRAGMENTARYRANGE - 1,553 MILES WING SPAN - 100 INCHESLENGTH - 219 INCHES WEIGHT - 4,190 POUNDSENGINE - SOLID PROPELLENT BOOSTER/TURBOJET CRUISE one Williams F107-400 rated at 600 lbs. thrustGUIDANCE - TERCOM, GPS, DSMAC AND INFRA-REDSPEED - 550 MILES PER HOURCOST - $1.4 MILLION TO OVER $2 MILLION DEPENDING ON VERSION
Tomahawk
Fig. (1.11) Active homing system
Fig. (1.12) Semiactive homing system
Fig. (1.11b) Passive homing system
Fig. (2.5) Pitch angle dynamic response for 3D and 2D models, linearization was performed at frequency =.2 (rad/s)
Fig. (2.6) Cross coupling effect (yaw * pitch)
Fig. (2.7) Mean value of cross coupling effect versus input frequencies at constant amplitude of .1(rad) rudder deflection
Fig. (3.1) Single-Input neuron The neuron output is calculated as:A=f(wp+b)
Fig. (3.2) Multiple-Input Neuron
NEURAL NETWORK FOR MISSILE GUIDANCE
Fig. (3.5) Control by Direct Inverse Control at steady state flight
Fig. (3.6) Control by Direct Inverse Control at climb angle =90 deg
Fig. (3.7) Control by Direct Inverse Control at initial velocity =700 m/s
Fig. (3.8) Control by Feedforward
Fig. (3.9) Control by Feedforward at steady state flight
Fig. (3.10) Control by feedback linearization
Fig. (4.1) The structure of a Fuzzy Logic Control System
Fig. (4.2) membership functions
MISSILE GUIDANCE USING FUZZY LOGIC CONTROL
Region 1 2 3 4IF error is P N N P
AND error rate is N N P PTHEN control signal (δu) is Z N Z P
Table (4.1) Control rules for a simple generic fuzzy controller
Fig. (4.12) Step response of a simple generic fuzzy controller
Fig. (4.13) Fuzzy inference system control action
Fig. (4.14) Comparison between Fuzzy logic controller and NN controller
ISE
Fig. (4.15) Effect of normalized and denormalized factors
Fig. (4.16) Comparison between PD and FL controllers with combined sine wave input for pitch motion
Fig. (4.17) Integral square error for PD and FL controllers
Fig. (4.19) Integral square error for different Fuzzy logic controllers using low bypass filter
Fig. (4.22) Effect of using NNC for the first 5 seconds
Fig. (4.23) Error, error rate and control signal
Fig. (4.28) Fusion Fuzzy system output k1
Fig. (4.29) Fusion Fuzzy system output k2
Fig. (4.30) Fusion Fuzzy system output k3
Fig. (4.31) Comparison between PD and FL controllers with combined sine wave input
Fig. (4.32) Integrated square error for PD and FL controllers with combined wave input
Fig. (4.33) Angle of attack using FLC with combined sine wave input
Fig. (4.34) Elevator deflection angle using FLC with combined sine wave input
Fig. (4.35) Absolute velocity of the missile using FLC with combined sine wave input
Fig. (4.36) Comparison between fuzzy logic and PD controllers for 0.3 rad step input
Fig. (4.37) Comparison between FLC and PD controller for 0.3 rad step response
Fig. (4.38) Elevator deflection angle for 0.3 step input using FLC
Fig. (4.39) Angle of attack for 0.3 step input using FLC
Fig. (4.40) Absolute velocity for 0.3 step input using FLC
Fig. (4.41) Path angle response using PD, FL controllers with ramp input
Fig. (4.42) Comparison between integrated square error for PD, FL controllers with ramp input
Fig. (4.43) Angle of attack using FLC with ramp input
Fig. (4.44) Missile velocity using FLC with ramp input
Computer simulation
1- F.O.R to body axis transformation
2- Aircraft-missile tracking paths
3- Results and evaluation procedure
4- Final conclusion
Fig. (5.1) AIM 9 Model with 2 separate controllers
Fig. (5.1) AIM 9 Model with 2 separate controllers
Fig. (5.3) Actual trajectories for target and missile
HYBRID NEUROFUZZY CONTROL SYSTEM FOR MISSSILE GUIDANCE
Fig. (5.4) 3D Plot for missile trajectory in both X,Y axes with respect to time
Fig. (5.5) 3D Plot for missile trajectory in both X, Z axes with respect to time
Fig. (5.8) Error in Y direction due to SSA, control system error
Fig. (5.7) Error in Z direction due to AOA, control system error
Table (6.1) ISE for PD, NN, FL controllers
Controller type Step input Ramp input Combined sine wave input
PD controller .00388 .00633 .112NNC --- --- 1.75FLC .00318 .00580 .066
Table (6.2) Comparison between combined (NNC, FLC) and fuzzy logic controllers for combined sine wave input.Type of controller ISE after
1 SecISE after 2 Sec
ISE after 3 Sec
ISE after 4 Sec
ISE after 5 Sec
ISE at the end of the mission
Combined controller
.009 .011 .020 .027 .035 .112
FLC .017 .020 .032 .034 .035 .066
6.4 Recommendations for Future Work
1-Practical application of the control techniques in this work in collaboration with the ministry of defense is highly recommended.
2-Optimization of the guidance system of the air-to-air missile according to the target maneuver data (predicted path, velocity and acceleration). This will require multiple types of guidance techniques verifying the best performance at any target-missile regime.