The Development of Neural Network Based System Identification ...

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194 CHAPTER 7 FLIGHT CONTROL SYSTEM DESIGN: RESULTS AND DISCUSSION dynamic system [Shridhar and Cooper, 1997, Clarke et al., 1987, Garriga and Soroush, 2010]. However, the empirical tuning approach is the most preferred method since the tuning is based on the actual control performance which gives a better assignment of N p value compared with the approximation method. Table 7.4 The control performance comparison with various N p values. Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s) θ φ r N p = 8 0.40 1.78 39.20 0.26 N p = 10 0.16 0.84 17.05 0.25 N p = 12 0.06 0.54 4.25 0.34 N p = 8 0.46 n/a n/a n/a N p = 10 0.12 n/a n/a n/a N p = 12 0.05 n/a n/a n/a N p = 8 6.51 n/a n/a n/a N p = 10 5.50 n/a n/a n/a N p = 12 7.12 n/a n/a n/a The tuning results for the yaw angle control are given in Figure 7.6 and Table 7.5. The yaw angle control is achieved by giving r ref = K ψ (ψ ref − ψ m ) as a reference trajectory to the NNAPC yaw rate controller. The variables ψ ref and ψ m denote the reference and the measured yaw angle respectively. In this test, the automatic controller actions are deactivated for the roll and pitch channels while allowing only the automatic yaw rate controller to compensate for the trajectory error in the yaw channel. The servomechanisms are also locked for the pitch and roll channels. The yaw controller performance is tuned with r w = 1.5 and N p = 10 during this test. The result obtained suggests that the gain value of K ψ = 0.5 gives the lowest rise time with high MSE value, which indicates a high steady state error in the system response. The yaw controller gives better compensation performance and faster response as the gain value is increased to K ψ = 1. Table 7.5 The control performance comparison with various K ψ values. Tuning parameter MSE Settling Time (s) Overshoot (%) Rise Time (s) ψ K ψ = 0.5 11.20 3.73 1.46 5.02 K ψ = 0.75 8.92 2.48 1.61 2.16 K ψ = 1.0 1.86 1.83 1.31 1.48
7.3 EXPERIMENTAL RESULTS 195 140 Yaw Angle (Degree) 120 100 80 60 Reference K ψ = 0.5 K ψ = 0.75 K ψ = 1.0 40 0 100 200 300 400 500 600 700 Sample (k) Tail Rotor Collective Pitch 0.2 0.1 0 −0.1 −0.2 0 100 200 300 400 500 600 700 Sample (k) Figure 7.6 The NNAPC response comparison with various K ψ values for the yaw angle compensation. The prediction horizon N p = 10 and control penalty factor r w = 1.5 are selected for this controller with constraints on control input rate −0.015 ≤ ∆u(k) ≤ 0.015. 7.3.2 4-DOF Controller The optimal controller parameters determined from the previous flight tests are used for controlling four of the helicopter control channels simultaneously under hovering flight. The final tuning parameters for the NNAPC controller with the recursive NN training are given in Table 7.6. The experiment is performed in the following steps: Initially, the helicopter is placed in the start-up position with yaw rotation axis locked using the servomechanism. Then, the throttle command of the helicopter is increased gradually from 0 to 0.25, in order to gain enough main rotor speed. The controller actions for the pitch, roll and yaw channels are then activated to drive the helicopter around level orientations in pitch and roll channels, while the yaw controller tracks a constant zero angular speed in yaw motion. After the level flight condition is obtained, the yaw rotation locking mechanism is then released to allow the helicopter to move freely in the yaw axis. The throttle is then increased to create enough lift, which is determined around 0.65. Next, the altitude controller is activated to drive the helicopter to track a 10 cm altitude reference. Figure 7.7 shows the hovering controller developed
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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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4.4 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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Page 217 and 218: 7.2 FLIGHT TESTS IMPLEMENTATION 185
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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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