The Development of Neural Network Based System Identification ...

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152 CHAPTER 5 NN BASED SYSTEM IDENTIFICATION: RESULTS AND DISCUSSION networks are lower than the MLP network, the prediction performance of both NN models are on par with the prediction quality of the MLP network. The results also point out that the average RMSE and the confidence limit values from each of the proposed networks are able to produce prediction that adequately fit with the test data. The prediction from the MLP, HMLP and modified Elman networks are also shown to be robust from large variations of weights values. Therefore, it can be concluded that NN training from the off-line training methods such as the LM algorithm or repeated GN method are able to produce satisfactory prediction performance even with large deviation of weights set derived from the training process. Validation results conducted in this study confirmed that the off-line NN model is suitable for modelling the helicopter’s attitude dynamics correctly. However, the dynamic model identified using the off-line NN model has a major drawback such that the method’s inability to represent the entire flight operation very well because of the time varying nature of helicopter flight dynamics. Recursive type training such as the rGN method was used in this study to overcome such problems in system identification. Results indicate that the rGN algorithm is more adaptive to the changes in dynamic properties, although the generalisation error of repeated rGN is slightly higher than off-line LM method. The rGN method is also found capable of producing a satisfactory prediction quality even though the model structure was incorrectly selected. The generalisation and adaptability performance of the model can be further improved by properly selecting the optimised network structure with the aid of k-fold cross validation method. The recursive method presented in this work is suitable for modelling the helicopter in real-time within the control sampling time and computational resource constraints. Recursive Gauss-Newton method used in the NN training demonstrates faster prediction updates and offers rapid computation of weight adaptation with average training time of 3.88 ms. The average training time for rGN algorithm is well below the targeted control loop sampling period (22 ms). This indicates that such recursive training algorithms are well suited for real-time applications. Furthermore, the proposed HMLP and modified Elman networks are found to improve the learning rate of NN prediction and this
5.8 SUMMARY 153 would enable the implementation of the real time recursive computation of the NN based system identification models. These models can be further used for the design of adaptive flight controllers for autonomous flight. In the next chapter, the theoretical foundation of the NN based flight controller design is presented.
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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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Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
Page 219 and 220: 7.3 EXPERIMENTAL RESULTS 187 1 0.8
Page 221 and 222: 7.3 EXPERIMENTAL RESULTS 189 Longit
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 233 and 234: 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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