The Development of Neural Network Based System Identification ...

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xxx LIST OF TABLES FIR FPGA GCS GN GNC HJB HMLP IMC IMU LM LUT MIMO MLP MNN MPC MSE NBN NI NMPC NMSS NN NNAPC Finite Impulse Response Field-Programmable Gate Array Ground Control Station Gauss-Newton Guidance, Navigation and Control Hamilton-Jacobian-Bellman Hybrid Multi-Layered Perceptron Internal Model Controller Inertial Measurement Unit Levenberg-Marquardt Look-Up-Table Multiple Inputs-Multiple Outputs Multi-Layered Perceptron Memory Neural Network Model Predictive Control Mean Square Error Neuron by Neuron algorithm National Instrument Non-linear Model Predictive Control Non-Minimal State Space Neural Network Neural Network based Predictive Control
LIST OF TABLES xxxi NNARX QP RBF RC rGN RHC rLM RMSE RNN RPE SAS SCARA SD SISO SISO SOFM TITO UART UAS UAV Neural Network ARX Quadratic Programming Radial Basis Function Remote Control Recursive Gauss-Newton method Receding Horizon Control Recursive Levenberg-Marquardt Root Mean Square Error Recurrent Neural Network Recursive Prediction Error method Stability Augmentation System Selective Compliant Articulated Robot Arm Steepest Descent Single Input and Single Output Single Input-Single Output Self-Organising Feature Map Two Inputs-Two Outputs Universal Asynchronous Receiver/Transmitter Unmanned Aerial System Unmanned Aerial Vehicle
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
Page 19 and 20: LIST OF FIGURES xix 3.13 Overview o
Page 21 and 22: LIST OF FIGURES xxi 5.2 The predict
Page 23 and 24: LIST OF FIGURES xxiii 5.13 The pred
Page 25 and 26: LIST OF FIGURES xxv 7.1 The locatio
Page 27 and 28: LIST OF TABLES 3.1 The specificatio
Page 29: NOMENCLATURE AFCS Automatic Flight
Page 34 and 35: 2 CHAPTER 1 INTRODUCTION Surveillan
Page 36 and 37: 4 CHAPTER 1 INTRODUCTION is then de
Page 38 and 39: 6 CHAPTER 1 INTRODUCTION the dynami
Page 40 and 41: 8 CHAPTER 1 INTRODUCTION models [Sa
Page 42 and 43: 10 CHAPTER 1 INTRODUCTION improved
Page 44 and 45: 12 CHAPTER 1 INTRODUCTION Chapter 3
Page 47 and 48: Chapter 2 LITERATURE REVIEW The pur
Page 49 and 50: 2.1 INTRODUCTION 17 • Inefficient
Page 51 and 52: 2.1 INTRODUCTION 19 Figure 2.2 The
Page 53 and 54: 2.2 HELICOPTER DYNAMICS MODELLING A
Page 59 and 60: 2.3 NEURAL NETWORK BASED SYSTEM IDE
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2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
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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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