The Development of Neural Network Based System Identification ...

More documents

Recommendations

Info

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
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
Page 19 and 20:
LIST OF FIGURES xix 3.13 Overview o
Page 21 and 22:
LIST OF FIGURES xxi 5.2 The predict
Page 23 and 24:
LIST OF FIGURES xxiii 5.13 The pred
Page 25 and 26:
LIST OF FIGURES xxv 7.1 The locatio
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
Page 34 and 35:
2 CHAPTER 1 INTRODUCTION Surveillan
Page 36 and 37:
4 CHAPTER 1 INTRODUCTION is then de
Page 38 and 39:
6 CHAPTER 1 INTRODUCTION the dynami
Page 40 and 41:
8 CHAPTER 1 INTRODUCTION models [Sa
Page 42 and 43:
10 CHAPTER 1 INTRODUCTION improved
Page 44 and 45:
12 CHAPTER 1 INTRODUCTION Chapter 3
Page 47 and 48:
Chapter 2 LITERATURE REVIEW The pur
Page 49 and 50:
2.1 INTRODUCTION 17 • Inefficient
Page 51 and 52:
2.1 INTRODUCTION 19 Figure 2.2 The
Page 53 and 54:
2.2 HELICOPTER DYNAMICS MODELLING A
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
2.3 NEURAL NETWORK BASED SYSTEM IDE
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85:
2.5 SUMMARY 53 design based on the
Page 88 and 89:
56 CHAPTER 3 AERIAL PLATFORM AND CU
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 109 and 110:
Chapter 4 NEURAL NETWORK BASED SYST
Page 111 and 112:
4.2 THE ARTIFICIAL NEURAL NETWORKS
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
4.3 SYSTEM IDENTIFICATION WITH NEUR
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
4.4 SUMMARY 117 Segment 1 Segment 2
Page 151 and 152:
Chapter 5 NN BASED SYSTEM IDENTIFIC
Page 153 and 154:
5.2 OFF-LINE BASED SYSTEM IDENTIFIC
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172: 5.4 OFF-LINE BASED SYSTEM IDENTIFIC
Page 177 and 178: 5.6 ON-LINE SYSTEM IDENTIFICATION 1
Page 179 and 180: 5.6 ON-LINE SYSTEM IDENTIFICATION 1
Page 181 and 182: 5.7 MODEL PERFORMANCE COMPARISON US
Page 183 and 184: 5.8 SUMMARY 151 methods such as wei
Page 185: 5.8 SUMMARY 153 would enable the im
Page 188 and 189: 156 CHAPTER 6 NEURAL NETWORK BASED
Page 215 and 216: Chapter 7 FLIGHT CONTROL SYSTEM DES
Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
Page 219 and 220: 7.3 EXPERIMENTAL RESULTS 187 1 0.8
Page 221: 7.3 EXPERIMENTAL RESULTS 189 Longit
Page 225 and 226: 7.3 EXPERIMENTAL RESULTS 193 Flight
Page 227 and 228: 7.3 EXPERIMENTAL RESULTS 195 140 Ya
Page 229 and 230: 7.3 EXPERIMENTAL RESULTS 197 indica
Page 231 and 232: 7.3 EXPERIMENTAL RESULTS 199 20 100
Page 233 and 234: 7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
Page 235 and 236: 7.3 EXPERIMENTAL RESULTS 203 mainta
Page 237 and 238: 7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
Page 239: 7.4 SUMMARY 207 data measured from
Page 242 and 243: 210 CHAPTER 8 CONCLUSIONS AND FUTUR
Page 244 and 245: 212 CHAPTER 8 CONCLUSIONS AND FUTUR
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
show all

The Development of Neural Network Based System Identification ...

Create successful ePaper yourself

Delete template?

Save as template?