The Development of Neural Network Based System Identification ...

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156 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM addition of an integrator term into the state space model are presented in Section 6.4 and 6.5. Section 6.6 and 6.7 describe the model prediction process using the state space model and optimisation routine for NNAPC design. In Section 6.8, the NNAPC design with constraints is discussed in detail. The operational constraints in the optimisation step are introduced into NNAPC design to improve the performance of the control system. Then, the chapter presents the basic control architectures of helicopter UAS and NNAPC algorithm implementation in Section 6.9. The chapter is summarised in Section 6.10. 6.2 NN BASED APPROXIMATE PREDICTIVE CONTROL PRINCIPLES The main objective of the MPC implementation is to compute a trajectory of future manipulated variable u to optimise the future plant output ŷ over a specified time horizon [Camacho and Bordons, 2004, Rawlings, 2000]. Figure 6.1(a) shows the basic architecture of the NN based MPC which uses prediction from a NN model to generate future plant output. In this approach, a process model such as NN provides a prediction of the future plant response over the specified horizon before being fed into an optimisation process. The optimisation process is carried out within a limited time horizon by giving the initial plant dynamics at the start of the process. System operational constraints can also be incorporated into the optimisation process which improves the control system performance when the control signals or system states violate the operation constraints. The optimisation process will be responsible for finding the value of the manipulated variable u which minimises a specified performance criterion given the current state measurement and future reference trajectory. The optimisation procedure using multi-step ahead prediction from the non-linear NN model often results in demanding computation efforts [Witt et al., 2007]. This is due to the minimisation of non-convex optimisation cost criterion because of the introduction of a non-linear model such as NN model [Samal, 2009]. The optimisation problem becomes a complex non-linear programming problem with no guarantee of reaching global minimum as the prediction from the model is determined by the non-linear
6.2 NN BASED APPROXIMATE PREDICTIVE CONTROL PRINCIPLES 157 Predicted Plant Output, ŷ NN Model Reference MPC Optimiser Control input, u UAS Plant Output, y (a) Predicted Plant Output, ŷ Optimisation NN Model Reference NN Controller Control input, u UAS Plant Output, y (b) Figure 6.1 Different configuration of NN based Model Predictive Control (MPC): (a) Basic configurations of NN based MPC which used prediction from a NN model (b) NN based controller that mimics the MPC controller by learning the controller input selection by optimisation process relationship of the future inputs [Norgaard, 2000]. To derive the control law for MPC, a pure gradient based method as used in Section 4.3.4 for NN training can be used for the optimisation problem. However, the gradient based method in general has problems in finding the minimum solution in real-time. This is due to the complexity of the Gradient and Hessian matrix computations. The computation loading of the MPC is made even worse if a significant number of constraints are imposed on the solution of optimisation problem.
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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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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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