The Development of Neural Network Based System Identification ...

More documents

Recommendations

Info

184 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION separately since both dynamic motions are quite slow compared with the yaw dynamic. This is due to the effect of stabiliser bar that introduces an increased damping effect to the rotor [Mettler, 2003]. The yaw dynamics is tested in a separate test because of the fast dynamic response while the altitude control is tested in the final part of the tuning refinement stages. Similar to the yaw control test, the altitude control is also done in a separate test since the involvement of chaotic movement induced by the sudden throttle change can severely damage the helicopter if not properly executed. This leads to the following experimental options: 1. Test the pitch rotation under automatic control while locking roll and yaw rotations and keeping zero altitude. 2. Test the pitch and roll rotation under automatic control while locking yaw rotation and keeping zero altitude. 3. Test the pitch and roll controller while manually controlling yaw rotation and varying altitude levels. 4. Test the pitch, roll and yaw controller while keeping zero altitude. 5. Test the altitude controller while locking pitch, roll and yaw rotations. 6. Complete 4 DOF controller test. 7.2.1 Computation Time Improvement The core of the UAS control hardware is the NI sbRIO-9605 flight computer which provides a 400 MHz real-time processor with reconfigurable FPGA. The real-time sbRIO- 9605 flight controller handles the sampling of the IMU measurement, NN training (or prediction), data logging and the MPC controller calculation. The data logging module is set at the lowest priority and sampling rate, while the NN and NNAPC module are run at a higher rate. Experience shows that setting the sampling rate of the NN training and NNAPC module at 30 Hz are sufficient for hover (4 DOF) control. However, in the early development phase of the flight controllers, the 4 DOF NNAPC controllers with
7.2 FLIGHT TESTS IMPLEMENTATION 185 recursive NN training were found to be too computationally intensive to perform on the real-time processor. Several improvements are made in the software program to reduce the total computation time. The main improvements are listed in Table 7.1. The first improvement is in the implementation of Xsens ‘plug and play’ IMU driver. The original IMU driver from Labview R○ software spent considerable computation time on determining the data format, number of bytes and redundant error handling. Since the data format is known, these functions can be removed. The same principle applies to the NMSS model conversion and the Hildreth’s QP algorithm, where redundant functions are removed. Finally, a Look-Up-Table (LUT) design is implemented for calculation of Γ and Φ, to reduce the amount of calculation involved in the nested ‘For’ loops. Table 7.1 The list of improvements made in the control software program. Note that the timing statistics are obtained using profiling tools in Labview R○ . Type of improments Removed redundant functions from the IMU driver Removed redundant calculation in the NMSS model Removed redundant calculation: constraint violation check LUT design for the model prediction gains Sub-VI Xsens Mti IMU Driver Model Conversion ms/run ms/run % gain (old) (new) 114.15 4.29 96.2 1.73 0.35 79.8 Hildreth’s QP 1.33 1.07 19.6 MPC Gains 16.02 4.08 74.5 Besides these improvements, several design tips were followed to keep the computation time as low as possible. These design tips are: • The number of flight data samples transmitted to the ground control station is limited to 10 samples at a time to reduce network overhead. • Graphical visualisation of the output variables is avoided in the real-time processor in order to remove unnecessary computational overhead. The visualisation of the sensor measurement and control input monitoring is displayed on the ground control station via the network shared variables protocol.
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: 5.3 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: Chapter 7 FLIGHT CONTROL SYSTEM DES
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 ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?