The Development of Neural Network Based System Identification ...

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38 CHAPTER 2 LITERATURE REVIEW The desire for enhanced agile manoeuvres and accurate tracking control requires the unmanned helicopter system to be operated beyond the range of nominal operating conditions. In order to extend the capabilities and operating range of the linear controllers, the non-linear dynamics of the helicopter UAS can be linearised into sets of linear models each representing certain operating condition. The tuning of the controller gains for each linear condition is done via interpolation schemes using the gain scheduling approach. However, such a technique has been reported to suffer performance degradation when performing large amplitude manoeuvres [Kendoul, 2012, Mettler, 2003, Valavanis, 2007]. In addition to limitation of linear approaches, the control problem becomes much more challenging to solve as the helicopter operates under varying atmospheric disturbance. Although the gain scheduling approach is widely used in unmanned helicopter control, the tuning of controller’s gains can also be difficult due to the presence of faults in the system and non-linearity in the operating conditions [Vijaya Kumar et al., 2011]. This specific problem in the gain scheduling control can be overcome by considering recursive adaptive control methods. The adaptive controller is categorised under model based non-linear control where the control parameters are updated at every time step. This control strategy is implemented to rapidly respond to the varying flight conditions or component failure scenario. Under the adaptive control approach, the controllers are divided into two types of control configurations [Samal, 2009, Narendra and Parthasarathy, 1990]: (1) direct adaptive controllers, and (2) indirect adaptive controllers. Figure 2.8 shows the basic configuration structure of the direct and indirect adaptive control system for a dynamic system. In direct adaptive controllers, the control parameters are updated directly to minimise the tracking error. The calculation of the controller parameters or gains does not rely on the dynamics system to update the controller parameters. Whereas in the indirect adaptive control configuration, a dynamic model is used to predict the response of the dynamic plant. The prediction from the identification model is assumed to be identical to the actual response and this information is used to minimise the tracking error of the system.
2.4 AUTOMATIC FLIGHT CONTROL SYSTEM 39 Reference Model y m (t) - Reference r(t) e c Controller u(t) Plant y p (t) + e c (a) Reference Model y m (t) - Reference r(t) e c Controller u(t) Plant y p (t) + e c e i Identification y i (t) Model - + ei (b) Figure 2.8 The configuration of adaptive control system: (a) direct adaptive controller; and (b) indirect adaptive controller. 2.4.1 Neural Network Based Control Design for Unmanned Helicopter System In recent development of real-time adaptive control system, the neural network approach has gained popularity in the development of AFCS due to its ability to learn complex mapping from flight data sets. The NN calculation is parallel in nature, which leads to faster calculation speed in an intensive computation problems [Balakrishnan and Weil, 1996]. Furthermore, the NN has the ability to adapt well to varying dynamic properties which make it suitable for adaptive control application. Typical neural network based
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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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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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