The Development of Neural Network Based System Identification ...

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xxvi LIST OF FIGURES 7.11 The coupled roll-pitch controllers’ response comparison under changes in the collective pitch and throttle curve settings. 200 7.12 The yaw controllers response comparison under changes in the collective pitch and throttle curve settings. 201 7.13 The altitude controllers’ response comparison under the changes in test rig’s counterbalance weights. The dash-dotted line indicates the constraint limits imposed on the control input calculation. 202 7.14 The coupled roll-pitch NNAPC controllers’ response comparison under input disturbances. These disturbances are introduced one at a time with the control responses shown here being overlaps of two separate test runs.204 7.15 The NNAPC controllers’ response comparison under input disturbances for yaw and altitude channels. These disturbances are introduced one at a time with the control responses shown here being overlaps of two separate test runs. 205
LIST OF TABLES 3.1 The specification of TREX600 ESP helicopter. 57 3.2 RC receiver output channels 61 4.1 Potter’s Square Algorithm 113 5.1 The MLP neural networks model parameters. 127 5.2 Summaries of System Identification Error Statistics for MLP network model. 131 5.3 The average RMSE for various noise levels applied to optimum weights of MLP network (4 hidden neurons with 3 past outputs and 1 past input).132 5.4 The HMLP neural networks model parameters. 134 5.5 Summaries of Error Statistics of HMLP Network Model 137 5.6 The average RMSE for various noise levels applied to optimum weights of HMLP network (3 hidden neurons with 2 past outputs and 1 past input).138 5.7 The modified Elman network parameters. 140 5.8 The Summaries of Error Statistics for Modified Elman Network. 142 5.9 The average RMSE for various noise levels applied to optimum weights of the modified Elman network (4 hidden neurons). 143 5.10 Summaries of Error Statistics for model NN1, NN2 and NN3. 147 5.11 Training time comparison between mini-batch LM method and rGN method. The values in bracket indicate the total training error (% RMSE).149
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
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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
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Page 38 and 39: 6 CHAPTER 1 INTRODUCTION the dynami
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Page 47 and 48: Chapter 2 LITERATURE REVIEW The pur
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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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