The Development of Neural Network Based System Identification ...

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180 CHAPTER 6 NEURAL NETWORK BASED PREDICTIVE CONTROL SYSTEM START Initialise: Constraint Parameters: M, MPC Parameters: Np, Nc, Q, R u(0) Initialize Read IMU, I2C Data Read IMU, I2C Data Read Reference Signal Update u(t) Read u(t-1) HMLP NN Training · Obtain with formulation in Table 4.1 Update Model Prediction · Eq. (6.30) QP Hildreth · Eq. (6.43) – (6.44) · Implement first element of control sequences Instantaneous Linearisation · Eq. (6.6) – (6.15) NMSS Formulation · Eq. (6.16) – (6.21) SS Model Augmentation · Eq. (6.25) u(k) = u(k-1) + ∆u GCS Monitor Stop Condition Stop Condition END END Figure 6.5 The NNAPC algorithm flowchart with recursive HMLP model. The training method shown in this figure is based on the recursive Gauss-Newton (rGN) method.
6.10 SUMMARY 181 Labview R○ software from National Instrument. It starts with the initialisation of the NN and controller parameters. At the start of the iteration, the IMU (rotations) and I2C (altitude) modules collect data and produce the output measurement vector, y(k). The HMLP module receives the previous calculated control input u(k) and current output measurement y(k) and constructs a linearised NMSS model. The NN model implemented in this algorithm can be obtained either from off-line training or recursive training. The model prediction module receives this state space model and predicts the future behaviour of the helicopter dynamics. This prediction is later on used in the Hildereth’s quadratic programming to optimise the control problem objective function subject to specified constraints. Finally, the calculated control input is sent to the FPGA module to be executed. The calculated control input and the current output measurements are also sent to the ground control station (GCS) for monitoring purposes. 6.10 SUMMARY In this chapter, the theoretical foundation of the NNAPC algorithm was presented. This algorithm was proposed in order to overcome the computational limitation of the non-linear NN based MPC. Different components of the NNAPC algorithm such as: the principle of instantaneous linearisation of the NN model, formulation of the NMSS and the augmented state space model, the prediction operation, optimisation and constraints handling of the MPC algorithm were discussed. In the next chapter, the implementation results of the proposed system identification method and control algorithm are presented to validate the feasibility of the methods for autonomous hovering flight.
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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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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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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 233 and 234: 7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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Page 246 and 247: 214 REFERENCES B. W. Bequette. Non-
Page 248 and 249: 216 REFERENCES Konstantinos Dalamag
Page 250 and 251: 218 REFERENCES Wayne Johnson. Helic
Page 252 and 253: 220 REFERENCES B. Mettler, Mark B.
Page 254 and 255: 222 REFERENCES V. R. Puttige and S.
Page 256 and 257: 224 REFERENCES D.H. Shim. Hierarchi
Page 258 and 259: 226 REFERENCES Nikos Vitzilaios and
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