The Development of Neural Network Based System Identification ...

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42 CHAPTER 2 LITERATURE REVIEW Vijaya Kumar et al. [2011]. The theoretical results presented are validated using a non-linear 6 DOF helicopter simulation undergoing several agile flight manoeuvres. The NN controllers developed are found to perform well in the presence of gust, sensor noise and modelling uncertainty. Moreover, the NN controllers are shown to meet the frequency domain and phase delay requirements of the U.S. Army Aeronautical Design Standard for rotorcraft handling qualities (ADS-33E-PRF). Although the stability analysis has been developed for such a controller, no extensive experimental evaluation has been performed yet to test the performance of the NN controller. In Suresh and Sundararajan [2012], a feed-forward adaptive NN controller scheme was proposed for a helicopter performing highly non-linear manoeuvres. The feed-forward NN control strategy used a NN controller to aid a basic LQR controller as shown in Figure 2.10. The NN controller is based on the on-line learning RBF network which used the Lyapunov based rules update to achieve global stability and better tracking performance. The pre-training and prior selection of the number of hidden neurons that accurately represent the inverse model are not required for the on-line learning RBF network. Instead, the appropriate number of neurons is determined recursively through the use of neurons growing and pruning algorithms. In the growing algorithm, the neurons of the inverse model are allowed to grow based on the corresponding error signal thresholds. In order to reduce the complexity of the NN model, the pruning strategy is incorporated in the algorithm to delete the non-contributing neurons. The simulation studies carried out in this work used a non-linear 6 DOF helicopter model similar to BO-105 helicopter. The performance simulation results clearly show the superior handling qualities of the proposed NN controller at various flight speeds and operating conditions. The results also indicate that the proposed on-line learning NN controller was able to adapt to the dynamic changes and provide the necessary tracking performance in executing highly non-linear manoeuvres such as obstacle clearance and elliptic manoeuvres. The NN based adaptive linearisation approach is in principle similar to standard linearisation controller used in Isidori [1995] and Slotine and Li [1991]. Figure 2.11 shows the general implementation of NN based feedback linearisation approach. As shown in
2.4 AUTOMATIC FLIGHT CONTROL SYSTEM 43 Reference r(t) Reference Model y d (t) Basic Controller u b (t) u nn (t) u(t) Actuator Dynamics Plant e c + NN Controller y p (t) Sensor Dynamics - Figure 2.10 The general schematic diagram of an on-line training feed-forward NN based control where a neural network is added to the existing control system. the figure, the control input signal of the feedback linearisation approach consists of two signal components where the first component cancels out the non-linearities in the system while the second component is a linear state feedback controller that stabilises the system [Hagan and Demuth, 1999]. The first component of the control signal can be approximated using the NN approach. Kutay et al. [2005] implemented an adaptive output feedback control that employs feedback linearisation for a 3 DOF helicopter model control. This design approach permits adaptation to both parametric uncertainty and un-modelled dynamics. It also allows adaptation under known actuator characteristics including actuator dynamics and saturation. The control design approach employed in this work was implemented together with an on-line trained NN model that compensates for modelling error and a linear compensator that filters the tracking error. The control design was tested to control the pitch motion of the 3 DOF helicopter model using attitude feedback information obtained through a low resolution optical sensor. In Nakanishi and Inoue [2002] and Nakanishi et al. [2002], a NN based feedback linearisation controller was used to control the Yamaha RMAX helicopter. The parameters of the NN controller are trained using recursive type NN training method such as Powel’s Conjugate Direction algorithm. The performance of the proposed NN controller was tested with the use of Yamaha RMAX flight simulator and validated flight experiments. Four SISO type flight controllers (roll, pitch, yaw and altitude controllers) were independently designed and implemented in the flight experiments.
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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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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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