The Development of Neural Network Based System Identification ...

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178 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM Outer Loop Control Inner Loop Control Helicopter y ref y,v PID v φref PID u lat φ u lat Lateral Dynamic x ref x,u PID u θ ref PID u long θ u long Longitudinal Dynamic z ref PID z z w ref w PID u col u col Heave Dynamic ψref ψ PID r r ref PID u ped r u ped Yaw Dynamic State Estimation Figure 6.3 The helicopter control with cascaded control approach. Multiple SISO based PID controllers is used in the inner and outer loop. responsible for guidance and generation of attitude or velocity commands for the inner loop control to achieve the desired position targets. In this study, the input/output pairing for the inner loop control is selected based on suggestions in Valavanis [2007] and Mettler [2003]. The suggested input/output pair is also expressed in Figure 6.3. The overall control system architecture designed for the helicopter UAS consists of the control subsystems: a MIMO MPC with roll and pitch dynamics; a SISO MPC with yaw rate dynamics and a SISO MPC with altitude rate dynamics. The control system architecture is depicted in Figure 6.4. The control system architecture is selected as a TITO system considering coupling between lateral and longitudinal channels while yaw rate and altitude rate (heave) dynamics are treated separately [Mettler, 2003, Valavanis, 2007, Samal, 2009]. The arrangement of this controller architecture assumes that the tail rotor cyclic and the collective pitch do not influence the roll and pitch channel. In each of the control subsystems, a HMLP network is utilised to represent the dynamic model of the MPC. The training of these HMLP networks can be done using the off-line LM or recursive based GN training. In recursive based training, the initial weights of
6.9 CONTROL ARCHITECTURES AND IMPLEMENTATION 179 Figure 6.4 The unmanned helicopter control system architecture with NNAPC controller. the HMLP network can be initialised using the weights trained with the off-line training. The time regressor vectors for each of these HMLP networks are constructed after obtaining pitch, roll and yaw angle, yaw rate and altitude rate measurement from the on-board sensors. Then, the prediction outputs from each HMLP network are fed to the instantaneous linearisation block to obtain the non-minimal state space models. Finally, all of the estimated linear models are transmitted to MPC loops for the optimisation iteration process before the new input signals are constructed and sent to the actuators. The full implementation of the NNAPC control algorithm with recursive HMLP model is shown in the Figure 6.5. The control algorithm is implemented based on
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The Development of Neural Network B
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ABSTRACT This thesis presents the d
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vii the MLP network, the prediction
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PUBLICATIONS The following is a lis
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xiv TABLE OF CONTENTS CHAPTER 4 CHA
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LIST OF FIGURES 1.1 Examples of typ
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LIST OF FIGURES xix 3.13 Overview o
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LIST OF FIGURES xxi 5.2 The predict
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LIST OF FIGURES xxiii 5.13 The pred
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LIST OF FIGURES xxv 7.1 The locatio
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LIST OF TABLES 3.1 The specificatio
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NOMENCLATURE AFCS Automatic Flight
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LIST OF TABLES xxxi NNARX QP RBF RC
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2 CHAPTER 1 INTRODUCTION Surveillan
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4 CHAPTER 1 INTRODUCTION is then de
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6 CHAPTER 1 INTRODUCTION the dynami
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8 CHAPTER 1 INTRODUCTION models [Sa
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10 CHAPTER 1 INTRODUCTION improved
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12 CHAPTER 1 INTRODUCTION Chapter 3
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Chapter 2 LITERATURE REVIEW The pur
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2.1 INTRODUCTION 17 • Inefficient
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2.1 INTRODUCTION 19 Figure 2.2 The
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2.2 HELICOPTER DYNAMICS MODELLING A
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2.3 NEURAL NETWORK BASED SYSTEM IDE
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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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Page 181 and 182: 5.7 MODEL PERFORMANCE COMPARISON US
Page 183 and 184: 5.8 SUMMARY 151 methods such as wei
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Page 215 and 216: Chapter 7 FLIGHT CONTROL SYSTEM DES
Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
Page 219 and 220: 7.3 EXPERIMENTAL RESULTS 187 1 0.8
Page 221 and 222: 7.3 EXPERIMENTAL RESULTS 189 Longit
Page 223 and 224: 7.3 EXPERIMENTAL RESULTS 191 Pitch
Page 225 and 226: 7.3 EXPERIMENTAL RESULTS 193 Flight
Page 227 and 228: 7.3 EXPERIMENTAL RESULTS 195 140 Ya
Page 229 and 230: 7.3 EXPERIMENTAL RESULTS 197 indica
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Page 233 and 234: 7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
Page 235 and 236: 7.3 EXPERIMENTAL RESULTS 203 mainta
Page 237 and 238: 7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
Page 239: 7.4 SUMMARY 207 data measured from
Page 242 and 243: 210 CHAPTER 8 CONCLUSIONS AND FUTUR
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Page 246 and 247: 214 REFERENCES B. W. Bequette. Non-
Page 248 and 249: 216 REFERENCES Konstantinos Dalamag
Page 250 and 251: 218 REFERENCES Wayne Johnson. Helic
Page 252 and 253: 220 REFERENCES B. Mettler, Mark B.
Page 254 and 255: 222 REFERENCES V. R. Puttige and S.
Page 256 and 257: 224 REFERENCES D.H. Shim. Hierarchi
Page 258 and 259: 226 REFERENCES Nikos Vitzilaios and
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