The Development of Neural Network Based System Identification ...

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12 CHAPTER 1 INTRODUCTION Chapter 3 presents the overview of the helicopter platform and avionic systems used for the development of the autonomous helicopter UAS. For flight control testing purposes, a safety test rig has been developed to constraint the helicopter movement and avoiding fatal crashes which would arise from possible hardware failures or programming errors. Chapter 4 presents the procedures of the off-line system identification of helicopter dynamics using the neural network approach. Then, the on-line (recursive) training method is proposed to improve the adaptability of the predicted model. The prediction of the neural network model employed in this study is further improved using Hybrid Multi-Layered Perceptron (HMLP) and modified Elman networks which significantly reduce the number of weights in the network and reduce the required computation time. Chapter 5 presents the model selection and validation results from the proposed neural network based system identification methods. The optimal network structures for system identification are found using the proposed validation method used in the previous chapter. The performance of standard Multi-Layered Perceptron (MLP) is then compared to the HMLP and modified Elman network performance. Chapter 6 presents the model predictive control methodologies using the dynamic model identified using the neural network based system identification approach. It proposes solutions to the drawbacks of neural network based MPC real-time implementation by introducing the application of Neural Network based Approximate Predictive Control (NNAPC). The prediction of NNAPC is provided using linear models extracted from the identified NN model obtained from either off-line or on-line modelling techniques. Chapter 7 presents the flight tests implementation and validation results of the proposed adaptive flight controller. In this chapter, the tuning procedures of the NNAPC flight controller are presented. The proposed flight controller is designed in several development stages where the best tuning parameters obtained from
1.5 THESIS ORGANISATION 13 different stages are then used as the basic tuning values for hovering flight controller. Subsequently, the flight test results from the tuning procedures are then presented, including those from the dual channel control (pitch-roll), triple channel control (pitch-roll-yaw), altitude control and hovering control. Then, the proposed NNAPC flight controller performance is tested and validated under conditions such as dynamic parameter variations and input disturbances. Chapter 8 presents the conclusions about the research project and recommendations for future work.
Page 1: The Development of Neural Network B
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Page 29 and 30: NOMENCLATURE AFCS Automatic Flight
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62 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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