The Development of Neural Network Based System Identification ...

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192 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION Yaw Rate (deg/s) 50 40 30 20 N p = 8 N p = 10 N p = 12 Reference 10 0 −10 −20 0 50 100 150 200 250 300 350 Sample (k) Tail Rotor Collective Pitch 0.5 0 −0.5 0 50 100 150 200 250 300 350 Sample (k) Figure 7.4 channel. The NNAPC response comparison with r w = 1.5 and various N p values for the yaw rate feedback controller and the gyro sensor unit can be removed from the unmanned helicopter system since the autopilot system has utilised an IMU unit as the main attitude measurement and the NNAPC algorithm for the automatic yaw rate control. However, the gyro sensor and the commercial yaw rate feedback controller are kept in the unmanned helicopter system design to facilitate the manual control of the helicopter. Incorporating the commercial yaw rate feedback controller into the AFCS introduces a new control signal that increases the existing control input calculation from the NNAPC controller. Experience shows that this kind of controller arrangement makes the unmanned helicopter turn aggressively to the right when the yaw servomechanism is released in the beginning of the experiment. The sudden movement happens due to the added control signal value from the conventional yaw rate feedback controller that makes the actual tail rotor input movement ˆδ ped surge to the minimum deflection value (−1) in short period of time. In several runs, the unmanned helicopter could not even maintain the orientation in the yaw channel due to the tendency of the actual control input to get stuck at the minimum deflection value (−1). To prevent the helicopter
7.3 EXPERIMENTAL RESULTS 193 Flight Controller NNAPC Augmented Yaw Dynamic IMU PI Controller Yaw Dynamic Yaw Rate Gyro Commercial Yaw Rate Feedback Controller Figure 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. from spinning out of control in the yaw axis, a saturation limit is introduced in the NNAPC controller calculation that bounds the calculated control action value between −0.2 → 0.2. Table 7.4 summaries the controller performance for this comparison using the MSE, settling time, overshoot and rise time performance indicators. The result shows that the controller with N p = 10 and N p = 12 produces better response compared with N p = 8. The selection of prediction horizon length that is lower than N p = 8 would result in an unstable closed loop response. The prediction horizon N p parameter needs to be selected long enough to ensure that the closed loop system is stable and satisfies the control performance criteria [Maciejowski, 2002]. However, it is often impossible to choose a prediction horizon that predicts too long into the future as the solution can increase the computation time of the NNAPC controller. Moreover, the NNAPC optimisation is based on the prediction from the extracted linear models which are only valid in the linearisation window. Thus, the prediction from these linear models is not sufficiently accurate for prediction in the far future. Several alternative methods that can be used to approximate the N p value are based on the rise time or dead time information of the
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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 183 and 184: 5.8 SUMMARY 151 methods such as wei
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Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
Page 219 and 220: 7.3 EXPERIMENTAL RESULTS 187 1 0.8
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Page 223: 7.3 EXPERIMENTAL RESULTS 191 Pitch
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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
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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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