The Development of Neural Network Based System Identification ...

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190 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION Table 7.3 The control performance comparison with various r w values. Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s) θ φ r w = 1.0 1.9219 unstable unstable unstable r w = 1.5 0.1934 1.32 23.02 0.36 r w = 2.0 0.1975 3.80 47.45 0.59 r w = 1.0 10.26 n/a n/a n/a r w = 1.5 0.07 n/a n/a n/a r w = 2.0 0.75 n/a n/a n/a action is penalised too much in the cost function resulting in higher overshoot value. Next, the controllers’ performance is tested under the effect of prediction horizon N p variation. The experiment procedures for this test are similar to the previous experiment procedures. The only difference is that the test is performed under active roll, pitch and yaw controllers while the altitude channel is under manual control. Figure 7.3 and 7.4 show the results for comparing the controller performances under different values of N p . Both of the figures show the controller performance results for roll, pitch and yaw channels. The control penalty factor of r w = 1.5 is used in each of MPC controller setup. The same input constraints are used for roll and pitch controller as in the previous test, whereas, the constraint on the yaw rate controller is set up based on the rate of the control input, −0.015 ≤ ∆u(k) ≤ 0.015. Since the RC helicopter dynamics is unstable, several stability augmentation devices such as the mechanical stabiliser bar and active yaw rate feedback controller have been introduced to the RC helicopter system to enable human pilots to control the RC helicopter with ease. The stabiliser bar acts as a rate lagged feedback mechanism that dampens the roll and pitch control sensitivity due to the lateral and longitudinal cyclic inputs [Mettler, 2003]. The feedback mechanism would ease the manual control of the helicopter and at the same time reduce the destabilising effect on the helicopter due to wind gust or turbulence. In addition, the yaw rate feedback controller and a gyro sensor are also included in all RC helicopter models to improve the stabilisation of the helicopter in the yaw axis. Figure 7.5 shows the illustration of the link between the yaw dynamics of the helicopter and the component of the stability augmentation systems. Ideally, the yaw
7.3 EXPERIMENTAL RESULTS 191 Pitch Angle [Degree] 15 10 5 0 N p = 8 N p = 10 Reference N p = 12 −5 0 50 100 150 200 250 300 350 Sample (k) 1 Longitudinal Cyclic 0.5 0 −0.5 0 50 100 150 200 250 300 350 Sample (k) (a) Pitch angle θ Roll Angle (Degree) 6 4 2 0 −2 N p = 8 N p = 10 N p = 12 Reference 0 50 100 150 200 250 300 350 Sample (k) Lateral Cyclic 0.2 0.1 0 −0.1 −0.2 −0.3 0 50 100 150 200 250 300 350 Sample (k) (b) Roll Angle φ Figure 7.3 channels. The NNAPC response comparison with r w = 1.5 and various N p values for roll and pitch
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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 242 and 243: 210 CHAPTER 8 CONCLUSIONS AND FUTUR
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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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