The Development of Neural Network Based System Identification ...

More documents

Recommendations

Info

62 CHAPTER 3 AERIAL PLATFORM AND CUSTOM-BUILT FLIGHT TEST SYSTEM Gyro Tail Rotor Servo CH7/AUX2 CH4/RUDD Front Swash-plate CH6/AUX1 CH5/GEAR AR7000 Receiver CH1/THRO CH2/AILE CH3/ELEV ESC Brushless Motor Figure 3.5 Spektrum AR7000 receiver and actuator (servo) connections. between control inputs. For example, as collective is increased and the main rotor produces more lift, it also produces more torque for the tail rotor to counteract. The yaw damper senses the yaw created and sends a countering signal to the actuator, even if no input is commanded by the RC pilot. JR AVCS rate gyro is also useful when helicopter encounters a crosswind while hovering. When the helicopter drifts, the gyro generates a control signal to stop the drift and at the same time computes the drift angle and constantly outputs a control signal that resists the crosswind. Therefore the drifting of the tail can be stopped even if the crosswind continues to affect the helicopter position (see Figure 3.6). The Spektrum DX7 transmitter contains two primary levers for vehicle control. The up-down movement of the right lever controls the throttle and collective pitch while left-right movement control the lateral cyclic. Left-right motion of the left lever of the transmitter controls the tail rotor, while up-down motion simultaneously controls the longitudinal cyclic. The Spektrum DX7 transmitter features mixing capabilities that adjust the tail rotor to roughly compensate for the change in torque created as the throttle and collective pitch increase. The DX7 transmitter also has the capability that
3.3 THE DEVELOPMENT OF TEST STAND FOR SAFE FLIGHT CONTROL TESTING 63 Heading Direction Crosswind Flying attitude is maintained without tail drift Figure 3.6 The gyro automatically corrects changes in the helicopter tail trim by crosswind. allows us to connect any two DX7 series transmitter together for the purpose of training a new pilot. In practice, one of the two transmitters is held by the instructor pilot (Master Transmitter) and the second transmitter serves as the Trainer Transmitter. As long as the instructor holds his trainer switch in the ‘ON’ position, the model will respond to the commands of the trainer transmitter allowing the trainee to fly the model. The Master Transmitter has full control of the model if the trainer switch on the instructor’s transmitter is left in its ‘OFF’ position. 3.3 THE DEVELOPMENT OF TEST STAND FOR SAFE FLIGHT CONTROL TESTING Controlling a remote control helicopter is difficult and careful experimentation is essential in building a working UAV helicopter prototype. For the testing of the autopilot system developed in this research, a six degree of freedom (DOF) test-bed for safe indoor helicopter flight was developed. It was mainly designed as a safety device for preventing crashes and out-of-control flight. There are already many test stands for helicopter control tests available in the literature, but most of these stands are limited to only a few DOF that cannot be used to simulate free flight of the UAV while on the test stand [Cetto et al., 2009, Amidi, 1996, Kim and Tilbury, 2004, Vitzilaios and Tsourveloudis, 2009, Vilchis et al., 2003]. Therefore there is a need for a development of a test rig that
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 59 and 60: 2.3 NEURAL NETWORK BASED SYSTEM IDE
Page 69 and 70: 2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
Page 85: 2.5 SUMMARY 53 design based on the
Page 88 and 89: 56 CHAPTER 3 AERIAL PLATFORM AND CU
Page 109 and 110: Chapter 4 NEURAL NETWORK BASED SYST
Page 111 and 112: 4.2 THE ARTIFICIAL NEURAL NETWORKS
Page 123 and 124: 4.3 SYSTEM IDENTIFICATION WITH NEUR
Page 145 and 146:
4.3 SYSTEM IDENTIFICATION WITH NEUR
Page 147 and 148:
4.3 SYSTEM IDENTIFICATION WITH NEUR
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:
Page 173 and 174:
Page 175 and 176:
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 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
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 and 222:
7.3 EXPERIMENTAL RESULTS 189 Longit
Page 223 and 224:
7.3 EXPERIMENTAL RESULTS 191 Pitch
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?