The Development of Neural Network Based System Identification ...

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xxiv LIST OF FIGURES 5.19 The comparison of the MLP network trained by off-line Levenberg- Marquardt (LM) method and MLP network trained with recursive Gauss- Newton (rGN) method against roll rate measurement. (a) NN model 1 (NN1) is trained with off-line LM algorithm. NN1 model structure is set with n y = 1 and n u = 2 with 4 hidden neurons (b) NN model 2 (NN2) is trained with recursive Gauss-Newton algorithm. NN2 model structure is set with n y = 1 and n u = 2 with 4 hidden neurons; and (c) NN model 3 (NN3) is trained with recursive Gauss-Newton algorithm. NN3 model structure is set with optimised structure from k-fold cross validation (n y = 3 and n u = 1 with 4 hidden neurons). 146 5.20 The on-line prediction performance comparison for the MLP, HMLP and modified Elman networks. All of these networks are trained by rGN method with optimum model structure identified from off-line model identification. 150 6.1 Different configuration of NN based Model Predictive Control (MPC): (a) Basic configurations of NN based MPC which used prediction from a NN model (b) NN based controller that mimics the MPC controller by learning the controller input selection by optimisation process 157 6.2 The Neural Network based Approximate Predictive Control (NNAPC) scheme based on instantaneous linearisation of the NN model. 159 6.3 The helicopter control with cascaded control approach. Multiple SISO based PID controllers is used in the inner and outer loop. 178 6.4 The unmanned helicopter control system architecture with NNAPC controller. 179 6.5 The NNAPC algorithm flowchart with recursive HMLP model. The training method shown in this figure is based on the recursive Gauss- Newton (rGN) method. 180
LIST OF FIGURES xxv 7.1 The location of poles of the extracted linear models from the instantaneous linearisation process. The poles were obtained at every simulation interval and are super-positions in the z-domain plot. 187 7.2 The control response comparison with N p = 10 and different r w values for the roll and pitch channels. The dash-dotted lines indicate the constraint limits imposed on the control input calculation. 189 7.3 The NNAPC response comparison with r w = 1.5 and various N p values for roll and pitch channels. 191 7.4 The NNAPC response comparison with r w = 1.5 and various N p values for the yaw channel. 192 7.5 The block diagram showing the links between the on-board flight controller, the bare yaw dynamic and the components of augmented system in the yaw channel: the yaw rate gyro and the PI controller. G T indicates the actuator gain for the actuator that controls the tail rotor pitch while K ψ denotes the controller gain for yaw angle compensation. 193 7.6 The NNAPC response comparison with various K ψ values for the yaw angle compensation. The prediction horizon N p = 10 and control penalty factor r w = 1.5 are selected for this controller with constraints on control input rate −0.015 ≤ ∆u(k) ≤ 0.015. 195 7.7 The 4 DOF NNAPC controller with recursive NN training in action. 196 7.8 The control responses of the 4 DOF NNAPC controller with recursive NN training during hovering flight test. 197 7.9 The corresponding control signals from the 4 DOF NNAPC controller with recursive NN training during hovering flight test. 198 7.10 The parameter variation in collective pitch setting. (a) The original collective pitch and throttle setting from the RC transmitter which was used in the off-line NN training phase. (b) The new collective and throttle setting for controller comparison test under parameter variation. 199
Page 1: The Development of Neural Network B
Page 5 and 6: ABSTRACT This thesis presents the d
Page 7: vii the MLP network, the prediction
Page 11: PUBLICATIONS The following is a lis
Page 14 and 15: xiv TABLE OF CONTENTS CHAPTER 4 CHA
Page 17 and 18: LIST OF FIGURES 1.1 Examples of typ
Page 19 and 20: LIST OF FIGURES xix 3.13 Overview o
Page 21 and 22: LIST OF FIGURES xxi 5.2 The predict
Page 23: LIST OF FIGURES xxiii 5.13 The pred
Page 27 and 28: LIST OF TABLES 3.1 The specificatio
Page 29 and 30: NOMENCLATURE AFCS Automatic Flight
Page 31: LIST OF TABLES xxxi NNARX QP RBF RC
Page 34 and 35: 2 CHAPTER 1 INTRODUCTION Surveillan
Page 36 and 37: 4 CHAPTER 1 INTRODUCTION is then de
Page 38 and 39: 6 CHAPTER 1 INTRODUCTION the dynami
Page 40 and 41: 8 CHAPTER 1 INTRODUCTION models [Sa
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Page 44 and 45: 12 CHAPTER 1 INTRODUCTION Chapter 3
Page 47 and 48: Chapter 2 LITERATURE REVIEW The pur
Page 49 and 50: 2.1 INTRODUCTION 17 • Inefficient
Page 51 and 52: 2.1 INTRODUCTION 19 Figure 2.2 The
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2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
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2.5 SUMMARY 53 design based on the
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56 CHAPTER 3 AERIAL PLATFORM AND CU
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Chapter 4 NEURAL NETWORK BASED SYST
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4.2 THE ARTIFICIAL NEURAL NETWORKS
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4.3 SYSTEM IDENTIFICATION WITH NEUR
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4.4 SUMMARY 117 Segment 1 Segment 2
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Chapter 5 NN BASED SYSTEM IDENTIFIC
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5.2 OFF-LINE BASED SYSTEM IDENTIFIC
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.7 MODEL PERFORMANCE COMPARISON US
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5.8 SUMMARY 151 methods such as wei
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5.8 SUMMARY 153 would enable the im
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156 CHAPTER 6 NEURAL NETWORK BASED
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Chapter 7 FLIGHT CONTROL SYSTEM DES
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7.2 FLIGHT TESTS IMPLEMENTATION 185
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7.3 EXPERIMENTAL RESULTS 187 1 0.8
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7.3 EXPERIMENTAL RESULTS 189 Longit
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7.3 EXPERIMENTAL RESULTS 191 Pitch
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7.3 EXPERIMENTAL RESULTS 193 Flight
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7.3 EXPERIMENTAL RESULTS 195 140 Ya
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7.3 EXPERIMENTAL RESULTS 197 indica
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7.3 EXPERIMENTAL RESULTS 199 20 100
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7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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7.3 EXPERIMENTAL RESULTS 203 mainta
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7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
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7.4 SUMMARY 207 data measured from
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210 CHAPTER 8 CONCLUSIONS AND FUTUR
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212 CHAPTER 8 CONCLUSIONS AND FUTUR
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214 REFERENCES B. W. Bequette. Non-
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216 REFERENCES Konstantinos Dalamag
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218 REFERENCES Wayne Johnson. Helic
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220 REFERENCES B. Mettler, Mark B.
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222 REFERENCES V. R. Puttige and S.
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224 REFERENCES D.H. Shim. Hierarchi
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226 REFERENCES Nikos Vitzilaios and
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