The Development of Neural Network Based System Identification ...

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200 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION 15 Pitch Angle (Degree) 10 5 0 Reference NNAPC−rGN NNAPC−Offline −5 0 50 100 150 200 250 300 350 400 450 500 Sample (k) Longitudinal Cyclic 1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 50 100 150 200 250 300 350 400 450 500 Sample (k) (a) Pitch angle θ Roll Angle (Degree) 2 1 0 −1 −2 Reference NNAPC−Online NNAPC−Offline 0 50 100 150 200 250 300 350 400 450 500 Sample (k) Lateral Cyclic 0.1 0.05 0 −0.05 −0.1 −0.15 −0.2 −0.25 0 50 100 150 200 250 300 350 400 450 500 Sample (k) (b) Roll Angle φ Figure 7.11 The coupled roll-pitch controllers’ response comparison under changes in the collective pitch and throttle curve settings.
7.3 EXPERIMENTAL RESULTS 201 Yaw Rate (deg/s) 30 20 10 0 −10 −20 Reference NNAPC−Online NNAPC−Offline −30 0 50 100 150 200 250 300 350 400 450 500 Sample (k) Tail Rotor Collective Pitch 0.5 0 −0.5 0 50 100 150 200 250 300 350 400 450 500 Sample (k) Figure 7.12 The yaw controllers response comparison under changes in the collective pitch and throttle curve settings. Table 7.8 The controllers’ performance comparison under changes in the collective pitch and throttle curve settings. θ φ r Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s) NNAPC-Online 0.08 1.00 13.52 0.32 NNAPC-Offline 0.29 14.16 26.57 3.91 NNAPC-Online 0.05 n/a n/a n/a NNAPC-Offline 0.21 n/a n/a n/a NNAPC-Online 8.19 n/a n/a n/a NNAPC-Offline 9.34 n/a n/a n/a the helicopter carries. The compensation response comparison between the controllers under consideration is given in Figure 7.13 with the performance analysis for the test given in Table 7.9. The NN weights used in the NNAPC-Offline controller is trained based on the data measured with the original counterbalance weight (5 kg). The same NN weights obtained in the off-line training are also used in the NNAPC-Online controller as the network’s initial weights. The NNAPC-Online controller is found to be superior in terms of compensation performance compared with the NNAPC-Offline controller. An overshoot about 16.41% occurs in the NNAPC-Online controller response due to the inertia effect of the counterbalance weight. Even with the changes in counterbalance
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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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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
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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 235 and 236: 7.3 EXPERIMENTAL RESULTS 203 mainta
Page 237 and 238: 7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
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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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