The Development of Neural Network Based System Identification ...

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158 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM As an alternative to overcome this problem, a NN controller can be trained in off-line mode to mimic a conventional MPC controller. Subsequently this would reduce the computation and the tuning effort. An alternative configuration of NN based MPC is given in 6.1(b). In this configuration, the neural network controller is used to learn the controller input selection process calculated by the MPC optimisation algorithm. The training process of the NN controller is normally conducted off-line and at the end of the training process, the MPC optimisation step is replaced completely by the trained NN controller [Agarwal, 1997, Pottmann and Seborg, 1997]. However, this approach is impractical to implement as the method requires an MPC controller to be present before the implementation of the NN controller. Moreover, the NN controller performance could be compromised due to the nature of the off-line training, where certain segments of the flight operating range could not be properly represented. Furthermore, this approach is also unattractive to pursue since the NN controller needs to be retrained all over again whenever the configuration of MPC controller is modified. This chapter proposes a solution to the aforementioned drawbacks of NN based MPC real-time implementation by introducing the application of Neural Network based Approximate Predictive Control (NNAPC) using linear model prediction from identified NN model obtained either from off-line or on-line modelling techniques. Figure 6.2 shows the block diagram of NNAPC architecture. The NNAPC uses the linearised model extracted from the NN model through the principle of instantaneous linearisation [Norgaard, 2000]. Subsequently, this would make the NNAPC to be less computational and less demanding compared with other MPC techniques such as NN based MPC or non-linear model predictive control (NMPC) [Ogunfunmi, 2007]. Furthermore, the NNAPC design parameters are easy to tune and are found effective for a wide range of control applications and are suitable for systems with time delay [Norgaard, 2000, Witt et al., 2007, Lawrynczuk, 2007b]. The main difference between NNAPC with linear MPC and other NMPC methods lies in the prediction operation of NNAPC. Instead of relying on the prediction from single linear or non-linear model, the NNAPC controller extracts a linear model from the identified NN model at each sampling time and uses it to predict the future plant response within the specified time horizon.
6.2 NN BASED APPROXIMATE PREDICTIVE CONTROL PRINCIPLES 159 Linearized Model Parameters Model Prediction Extract Linear Model Predicted Plant Output, ŷ Optimisation NN Model Reference MPC Controller Control input, u UAS Plant Output, y Figure 6.2 The Neural Network based Approximate Predictive Control (NNAPC) scheme based on instantaneous linearisation of the NN model. As mentioned before, the objective of NNAPC controller is to bring the predicted output of the system ŷ as close as possible to a specified set-point r(k) at sample time step k. Here, the specified set-point is assumed to be constant in a single optimisation window. The best control parameter vector ∆U that reduces the error difference between set-point and predicted output over the N c control horizon is found by minimising the objective function J(k). The objective function J(k) that reflects the NNAPC objective is given as follows: J(k) = ( ) T ( ) R s − Ŷ R s − Ŷ + ∆U T ¯R∆U (6.1) where Ŷ denotes the future predicted output variables over prediction horizon N p and ∆U represents the future control trajectory over control horizon N c . In the case of SISO control, the dimension of the predicted output vector Ŷ is N p × 1 and the dimension of the control trajectory ∆U is N c × 1. Assuming that the dynamic system has m inputs, n 1 states and q outputs, the predicted output vector Ŷ and control trajectory ∆U are still represented in vector form for MIMO case with dimension qN p and mN c
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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
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Page 225 and 226: 7.3 EXPERIMENTAL RESULTS 193 Flight
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Page 239: 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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