The Development of Neural Network Based System Identification ...

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56 CHAPTER 3 AERIAL PLATFORM AND CUSTOM-BUILT FLIGHT TEST SYSTEM Figure 3.1 The TREX600 helicopter used in this research project with instrumentation equipment fitted between the fuselage and the landing gear. 3.2 AIR VEHICLE DESCRIPTIONS The unmanned aerial vehicle (UAV) platform which was used in this research is a conventional electric model helicopter known as TREX600, manufactured by ALIGN Corporation. The helicopter model was selected due to its sufficient payload capacity, great manoeuvrability and low cost replacement parts. It was equipped with a standard Bell-Hiller stabiliser bar on the main rotor, which improves handling characteristics for human pilots by increasing the damping on the pitch and roll responses. Furthermore, TREX600 was also equipped with a high efficiency high torque brushless DC motor that allows the helicopter to carry about 2 kg payloads with an operation time of about 15 min. The basic UAV platform shown in Figure 3.1 had been modified to make room for installing necessary electronic equipment which gathers flight data for dynamic modelling and control system design. Some key physical parameters of TREX600 RC helicopter are given in Table 3.1. This helicopter consists of a fuselage, a main rotor, a tail boom/tail rotor assembly and landing skid. The main rotor and tail rotor are driven by an electric brushless DC motor which is mounted under the transmission casing for compact design. This prohibits the mounting of any avionic systems in the area underneath transmission
3.2 AIR VEHICLE DESCRIPTIONS 57 Table 3.1 The specification of TREX600 ESP helicopter. Specifications TREX600 ESP Length 1.16 m Height 0.4 m Main Blade Length 0.6 m Main Rotor Diameter 1.35 m Tail Rotor Diameter 0.24 m Weight 3.3 kg Endurance 15 min casing and leaves this vehicle less attractive for tight component installation. The main structure of the helicopter consists of two vertically mounted parallel main frames made of carbon fibre, battery and gyro mounts (plastic), metal motor mount and a metal bottom bracket. This simple structure design produces minimum weight with maximum strength for the helicopter platform. The electric motor and associated reduction gearing are mounted in between these two carbon fibre main frames, with various accessories and control components attached where appropriate as shown in Figure 3.2. The factory also provided a lightweight plastic landing skid which was connected to the metal bottom bracket via four mounting points. The RC helicopter usually has a very high rotor speed of around 1500 rpm and fast dynamic response due to its small inertia value. Shim [2000] had reported that in order for scaled model helicopters to achieve equilibrium of lift on the rotor disc in less than one rotor revolution, most of the small size helicopters would require stabilisation response time to be achieved in less than 40 ms. Without any extra stability augmentation devices, this is an extremely short time for radio controlled (RC) pilots to control the helicopter. For this reason, almost all small-size RC helicopters have a mechanism to artificially introduce damping. In most RC model helicopters, a large control gyro with an air-foil, referred to as a stabiliser bar or fly-bar is used to improve the stability characteristic around the pitch and roll axes and to minimise the actuator force required to control the main rotor cyclic pitch [Kim and Tilbury, 2004]. In addition, an electronic gyro is also used with the tail rotor input command to further stabilise the yaw axis. According to Mettler et al. [2002a], the stability augmentation system can be
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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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4.3 SYSTEM IDENTIFICATION WITH NEUR
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4.4 SUMMARY 117 Segment 1 Segment 2
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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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