The Development of Neural Network Based System Identification ...

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4 CHAPTER 1 INTRODUCTION is then designed for each different flight condition such as hovering flight or forward flight at different velocities. Since we have multiple controllers that provide satisfactory control for different operating points, gain scheduling approach is used to determine the current flight operating region and to activate the appropriate linear controller. Several measured variables from the on-board instrumentation such as airspeed, dynamic pressure or altitude can be used to trigger a specific linear controller relative to the current flight operating region. However, such control technique suffers performance degradation when performing large amplitude manoeuvres [Mettler, 2003, Kendoul, 2012, Valavanis, 2007]. Furthermore, a scheduling control method can results in a control signal that jumps to different values at each model transition. This effect needs to be reduced in practice as the sudden increase in the control action can result in dangerous sudden movement of the helicopter [Joelianto et al., 2011]. In addition to limitation of linear approaches, the control problem becomes much more challenging to solve as the operation of the helicopter also exposes them to varying atmospheric disturbance. Numerous advanced non-linear controllers such as feedback linearisation, adaptive control and non-linear model based approaches have been suggested in the literature to overcome the limitation of linear approaches with successful implementation in real flight tests [Kendoul, 2012, Cai et al., 2010]. Kendoul [2012] further argues that even though numerous advanced non-linear control approaches were proposed to improve the AFCS performance, significant improvement in flying capabilities has not been achieved with the non-linear controllers when compared with standard linear controllers. This is due to the fact that the linear controllers such as the widely used PID, LQR and H ∞ methods are robust and mature enough for the helicopter based control application. However, Kendoul [2012] in his review proposed that certain aspects of AFCS development can be further investigated and improved. The lists of AFCS developments that are required for further improvement are given as follows: • The development of a general purpose, flexible and self-tunable flight controller that can be deployed into different UAV airframes in a shorter development time. Such a controller design should include an adaptation mechanism to the internal
1.2 PROBLEM STATEMENT 5 model after physical changes occur (new installation of sensors or additional payload). • The development of a robust flight controller that can perform well in strong disturbances such as windy or severe weather conditions. • The development of a reconfigurable flight controller that redefines the control strategies based on flight modes, mission conditions and fault scenario. The capability of the helicopter based UAS flight controller can be further improved with the development of self-tunable and flexible controllers based on the neural network approach. The learning based method using neural network is well known to be a universal approximator, and hence is able to map complex input-output relationships using the test data [Hornik et al., 1989]. This unique ability of the neural network approach provides a good basis for development of a flexible and self-tunable flight controller for different rotorcraft platforms. Besides the ability to learn complex mapping, the neural network based controller can further be equipped with adaptive capability to parametric uncertainty and unknown flight dynamics. This leads to better controller performance under varying flight operating conditions since the adaptation capabilities in model estimation should give better accuracy of estimation of the non-linear process. The majority of the development process of helicopter systems has evolved around the dynamics modelling and control system design which contributes around 25-50% of total development work [Padfield, 2007]. The rotorcraft dynamic should be sufficiently modelled in various operating conditions to prevent major control performance degradation resulting from the poor predictive capability of the mathematical model developed. Padfield [2007] points out that poor model prediction can result in redesign efforts to improve or fix problems arising from poor flight performances and flying qualities. Subsequently, this leads to increased usage of resources, time and cost due to redesign efforts. It is an undeniable fact that the helicopter is a complex dynamic system and the modelling task requires a large amount of effort and extensive modelling skills. The first principle modelling method based on Newton laws are commonly used to infer
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
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Page 27 and 28: LIST OF TABLES 3.1 The specificatio
Page 29 and 30: NOMENCLATURE AFCS Automatic Flight
Page 31: LIST OF TABLES xxxi NNARX QP RBF RC
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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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