The Development of Neural Network Based System Identification ...

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68 CHAPTER 3 AERIAL PLATFORM AND CUSTOM-BUILT FLIGHT TEST SYSTEM 5V Rotary Encoder 1 Rotary Encoder 2 DI/CH A DI/CH B DI/CH A DI/CH B sbRIO 9605 Real-time Processor PCI BUS Reconfigurable FPGA COM Port Ethernet Port RS232 DIO/SDA DIO/SCL Ethernet IMU A/D Converter 5V Wireless Receiver Linear position transducer ONBOARD 5V 5V 5V Control Updates DO/THRO 12V ESC Electric Motor RC Receiver 5V DI/THRO DI/AILE DI/ELEV DI/RUDD DI/AUX1 DI/AUX2/Gyro Gain Multiplexer DO/AILE DO/ELEV DO/AUX1 DO/RUDD DO/AUX2/Gyro Gain Yaw Rate Gyro Servo 1 Servo 2 Servo 3 Servo 4 5V 5V 5V 5V DI/GEAR/Input Selector GROUND STATION RC Pilot RC Transmitter Monitoring Station Wireless Router Figure 3.10 The overall architecture for the developed automatic flight controller system. The communication between the on-board computer and the GCS is done via wireless network. 3.4.1 Positioning and Orientation System For indoor testing of the automatic flight control system, the safety test rig had been equipped with several rotary encoders and a linear position transducer to give us sensory information about the position of the helicopter UAV. Furthermore, an inertial measurement unit (IMU) was also installed on the helicopter to obtain attitude information of the aerial system. The motivation to use the linear position transducer and
3.4 FLIGHT INSTRUMENTATION SETUP FOR AUTOMATIC FLIGHT CONTROL TEST 69 Figure 3.11 Helicopter avionics and sensors on the test stand. rotary encoders instead of vision based localisation system was to avoid high development cost incurred from the use of high cost cameras [Vitzilaios and Tsourveloudis, 2009, Valenti et al., 2006, 2007]. Figure 3.11 shows the arrangement of positioning and rotational sensors in the test stand. Two rotary encoders are attached at each joint of arm 1 and 2 in the test stand. These encoders are set to zero during the initialisation phase and produce signed numbers that indicate the current position relative to the initial position. A positive number gives a rotation reading that indicates movement to the left, and vice versa, a negative reading indicates movement to the right. Measuring the rotational displacement of the joints between the centre pivot and the first arm (θ 1 ), and the first and second arm (θ 2 ), gives the location of the helicopter in the XY plane. To calculate the XY coordinate of the helicopter relative to the base and initial position, the following equation is used: x = L 1 cos (θ 1 ) + L 2 cos (θ 1 + θ 2 ) (3.1) y = L 1 sin (θ 1 ) + L 2 sin (θ 1 + θ 2 ) (3.2)
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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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Chapter 5 NN BASED SYSTEM IDENTIFIC
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5.2 OFF-LINE BASED SYSTEM IDENTIFIC
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.7 MODEL PERFORMANCE COMPARISON US
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5.8 SUMMARY 151 methods such as wei
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5.8 SUMMARY 153 would enable the im
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156 CHAPTER 6 NEURAL NETWORK BASED
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Chapter 7 FLIGHT CONTROL SYSTEM DES
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7.2 FLIGHT TESTS IMPLEMENTATION 185
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7.3 EXPERIMENTAL RESULTS 187 1 0.8
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7.3 EXPERIMENTAL RESULTS 189 Longit
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7.3 EXPERIMENTAL RESULTS 191 Pitch
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7.3 EXPERIMENTAL RESULTS 193 Flight
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7.3 EXPERIMENTAL RESULTS 195 140 Ya
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7.3 EXPERIMENTAL RESULTS 197 indica
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7.3 EXPERIMENTAL RESULTS 199 20 100
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7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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7.3 EXPERIMENTAL RESULTS 203 mainta
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7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
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7.4 SUMMARY 207 data measured from
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210 CHAPTER 8 CONCLUSIONS AND FUTUR
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212 CHAPTER 8 CONCLUSIONS AND FUTUR
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214 REFERENCES B. W. Bequette. Non-
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216 REFERENCES Konstantinos Dalamag
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218 REFERENCES Wayne Johnson. Helic
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220 REFERENCES B. Mettler, Mark B.
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222 REFERENCES V. R. Puttige and S.
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224 REFERENCES D.H. Shim. Hierarchi
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226 REFERENCES Nikos Vitzilaios and
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