The Development of Neural Network Based System Identification ...

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74 CHAPTER 3 AERIAL PLATFORM AND CUSTOM-BUILT FLIGHT TEST SYSTEM The Mobisense MBS270 embedded computer was basically used as the central processing board, primarily to collect and store all sensory data on the MicroSD card. The small computer board was equipped with a MicroSD storage card slot, serial interface for communication with the IMU and general purpose inputs/outputs for measurement and actuation purposes. Furthermore, the board offers faster software development since it runs on Linux OS, enabling higher-level programming in C/C++ with the supplied libraries. The board features the MMX instruction function set and Direct Memory Access for video processing which makes the board as an excellent platform for video processing tasks. The IMU unit which measures the linear acceleration, angular rates and Euler-angles of the helicopter was directly connected to the embedded system through an RS232 serial interface. This module can be programmed to run in polled mode (measurements start upon request) or in continuous mode. In continuous mode, the IMU transmits measurements to the host in the requested output format at the highest possible rate. For system identification purposes, it is recommended to use the Send Gyro-Stabilised Quaternion and Vectors (0×0C) output format in continuous mode. This would give noise free accelerations and quaternion readings that provide unique measurements for the attitude and the bias corrected angular rate vector. The highest possible output rate in this mode is 100 Hz, which can be achieved by modifying the default settings stored in the EEPROM address. The four pilot stick positions on the radio transmitter are the inputs in the system identification. During the experiment, the servo signals received from the transmitter signals were measured instead of direct measurements of the pilot stick deflections. There are four servomotors in the TREX600 actuation system, one of which controls the yawing movement of the helicopter. The remaining three servos which are arranged at 120 ◦ around the swash plate are used to apply a combination or individual efforts of lateral cyclic, longitudinal cyclic and collective pitch input movements. During the experiments, a separate micro-controller was used to continuously measure the servo signals. It passes the values through to the MBS270 over the UART serial interface.
3.6 SUMMARY 75 3.6 SUMMARY In this chapter, the air vehicle platform used for the system identification and control algorithm testing is discussed in terms of the vehicle specifications, structural, stability augmentation and actuation system characteristics. In order to ensure that flight controller tests were conducted in safe manner, a 6 DOF safety test stand was developed and equipped with necessary instrumentations that provide the end user with the helicopter’s position and attitude information. The benefit of having such a test system is that it would provide us with the ability to test the controller continuously regardless of the weather conditions. Subsequently, the development of such a device would also minimise the need for an experienced helicopter pilot while conducting flight tests. The system overview of flight instrumentation and avionics system used on the test stand is also discussed. At the end, the system overview of flight instrumentation developed for collecting data during system identification test is presented. The theoretical foundation and system identification methodology using neural networks approach are presented in the next chapter.
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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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2.2 HELICOPTER DYNAMICS MODELLING A
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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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