The Development of Neural Network Based System Identification ...

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202 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION 25 Altitude (cm) 20 15 10 NNAPC−Online Reference NNAPC−Offline 5 0 100 200 300 400 500 600 700 800 Sample (k) Collective Pitch 1.2 1 0.8 0.6 0.4 0.2 0 0 100 200 300 400 500 600 700 800 Sample (k) Figure 7.13 The altitude controllers’ response comparison under the changes in test rig’s counterbalance weights. The dash-dotted line indicates the constraint limits imposed on the control input calculation. Table 7.9 weight. z The altitude controllers’ performance comparison under changes in the counterbalance Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s) NNAPC-Online 0.01 3.644 16.41 1.385 NNAPC-Offline 0.53 16.72 12.88 3.25 weight, the controller is able to reformulate the appropriate control action to bring back the system to the reference point. On the other hand, the NNAPC-Offline controller produced higher rise time and did not settle well in respect to the reference trajectory. Next, the proposed NNAPC controllers are tested under the effect of input disturbance in each of the control channels. Introducing the disturbance effects to the calculated control inputs can be considered akin to the effect of wind gusts on the aerodynamic characteristics of the main rotor [Mettler, 2003]. It is important to test the controller compensation performance under input disturbances, in order to verify whether the proposed controller approach is able to operate in windy or disturbed conditions such as when the unmanned helicopter is flying in close proximity to structures. In this test, the helicopter is commanded to hover at 10 cm altitude reference, while
7.3 EXPERIMENTAL RESULTS 203 maintaining the level orientation (0 ◦ ) in roll and pitch axes. The yaw controller is tasked to maintain a zero angular rate reference. To simulate the efficiency of the NNAPC controllers in rejecting the input disturbances, the controller input values are added with constant input disturbances at each control channel. These input disturbances are introduced one a time in every control channel after the steady state condition is achieved in each control channel. Figure 7.14 and 7.15 show the controller response profiles when the constant input disturbances of −0.075, 0.075, 0.02 and −0.01 are introduced to disturb the control moves (δ lat , δ long , δ ped , δ col ) for a duration of 500 ms. It is shown that the controllers perform satisfactorily well under input disturbance effects with both controllers returned back to the reference conditions after the insertion of the disturbances within 2.8 s (86 samples) at the most. This demonstrates the robustness of the proposed NNAPC algorithms in rejecting sudden movement of the helicopter. Slight improvements can be seen for the control response of the NNAPC-Online controllers in the pitch and yaw axes where the control response returns to the reference value faster than the NNAPC-Offline controllers in 0.56 s (17 samples) and 0.26 s (8 samples) respectively.
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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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2.3 NEURAL NETWORK BASED SYSTEM IDE
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2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
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2.5 SUMMARY 53 design based on the
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56 CHAPTER 3 AERIAL PLATFORM AND CU
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Chapter 4 NEURAL NETWORK BASED SYST
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4.2 THE ARTIFICIAL NEURAL NETWORKS
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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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Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
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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
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Page 242 and 243: 210 CHAPTER 8 CONCLUSIONS AND FUTUR
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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
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