The Development of Neural Network Based System Identification ...

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xxviii LIST OF TABLES 7.1 The list of improvements made in the control software program. Note that the timing statistics are obtained using profiling tools in Labview R○ . 185 7.2 The HMLP network parameters used in the NNAPC controller implementation. 186 7.3 The control performance comparison with various r w values. 190 7.4 The control performance comparison with various N p values. 194 7.5 The control performance comparison with various K ψ values. 194 7.6 The final controller settings for the hovering flight test. 196 7.7 The 4 DOF NNAPC controller response under hovering flight. 197 7.8 The controllers’ performance comparison under changes in the collective pitch and throttle curve settings. 201 7.9 The altitude controllers’ performance comparison under changes in the counterbalance weight. 202
NOMENCLATURE AFCS Automatic Flight Control System ANN Artificial Neural Network AOA Angle of Attack ARX Auto Regressive structure with eXtra inputs AVCS Angular Vector Control System BFGS Broyden-Fletcher-Goldfarb-Shanno algorithm BPTT Back-Propagation Through Time CG Centre of Gravity CIFER R○ Comprehensive Identification from Frequency Responses software package CT Constant Trace DBP Dynamic Back-Propagation DE Differential Evolution algorithm DIO Digital Input-Output DOF Degrees of Freedom EFRA Exponential Forgetting and Resetting Algorithm ESC Electronic Speed Controller
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
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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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