The Development of Neural Network Based System Identification ...

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72 CHAPTER 3 AERIAL PLATFORM AND CUSTOM-BUILT FLIGHT TEST SYSTEM operation and 256 MB of non-volatile memory for storing programs and data logging. This device features a built-in 10/100 Mbit/s Ethernet port that can be use to conduct communication over the network. For sbRIO-9605 variant, only a RS232 serial port is provided to control peripheral devices. The main tasks of the real-time processor in the NI sbRIO-9605 are to gather sensory information from the IMU and to compute the necessary control update. On the other hand, the FPGA in the on-board computer system is programmed with Labview R○ software to functions as a data logger in charge of collecting positioning data from linear transducer, rotary encoders and servo inputs. It is also responsible for handling switching between autonomous control and manual control as well as updating the servo actuators according to the computed flight control laws. During manual flight mode, the helicopter is controlled remotely using standard RC equipment by human pilot. While in the autonomous flight mode, the helicopter movement will be directly controlled and supervised by the on-board flight controller. 3.4.3 Inertial Measurement Unit The IMU sensor is used to measure the orientation of the helicopter UAV in the body frame reference system. Attitude determination of a UAV is critical in order to ensure maintained flight for the autopilot system developed in this project. The Xsens MTi IMU was selected for UAV attitude measurement. It produces measurements of 16 calibrated states consisting of Euler angle (φ, θ, ψ), in quaternion representation (q 0 , q 1 , q 2 , q 3 ), angular rates in body coordinate frame (roll rate, p, pitch rate, q and yaw rate, r), body accelerations (A x , A y , A z ) and magnetic fields (m x , m y , m z ). The orientation of the MTi is computed by a Kalman filter algorithm for 3 DOF orientations. The Kalman filter algorithm uses signals from the rate gyroscopes, accelerometers and magnetometers to compute an optimal 3D orientation estimate of high accuracy with no drift for both static and dynamic movements. Figure 3.11 shows the location of this sensor on the helicopter. The MTi IMU outputs ASCII data at 100 Hz via RS232 protocol at 115 200 bit/s with no flow control. The IMU provides Euler angles representation ranging from +π rad to −π rad.
3.5 FLIGHT INSTRUMENTATION SETUP FOR SYSTEM IDENTIFICATION 73 Servo Signals TTL IMU Microstrain 3GDM-GX1 RS232 Microcontroller Arduino Duemilanove UART Embedded System Mobisense MBS 270 MicroSD Card FLASH Figure 3.13 Overview of the measurement system setup. The Euler angles representation suffer from the discontinuity problem when the body rotation angle rotates beyond 180 ◦ (or −180 ◦ ). This problem should not happen in roll and pitch axis as the helicopter UAS is not required to do inverted flight or aerobatic manoeuvres. However, this does not hold in the yaw rotation axis since the yaw angle has unlimited rotation. Modification to yaw angle measurement should be done in the software to eliminate the discontinuity problem in yaw axis at each sampling point. This can done through the use of geometry function in Labview R○ . 3.5 FLIGHT INSTRUMENTATION SETUP FOR SYSTEM IDENTIFICATION System development for this project was implemented in different stages which results in different instrument setup for both automatic flight control test and system identification test. The main purpose of system identification is to model or predict the helicopter response based on the collected flight data. In order to test the proposed system identification method, a much simpler data acquisition system is designed to record the necessary data during flight. The overall architecture overview of the data acquisition, shown in Figure 3.13, consists of a Mobisense MBS270 embedded computer, a Microstrain 3DM-GX1 Inertial Measurement Unit (IMU) and an Arduino Duemilanove microcontroller.
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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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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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