The Development of Neural Network Based System Identification ...

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120 CHAPTER 5 NN BASED SYSTEM IDENTIFICATION: RESULTS AND DISCUSSION 5.2 OFF-LINE BASED SYSTEM IDENTIFICATION FOR MLP NETWORK In this section, the off-line system identification proposed in Section 4.3.4 is implemented to identify the attitude dynamics of the UAS helicopter model. The identification process is conducted using real flight test data obtained from different flight manoeuvres. The flight test data were divided into training, validation and test data sets. The training and validation data sets were used for purpose of NN training and model structure selection. The test data set was used for the final evaluation of the NN model prediction accuracy and reliability. In each manoeuvre, only a certain input command was used to excite the dynamic of interest. During the experiment, all control inputs and all UAS state measurements were recorded and sampled at 100 Hz. Figure 5.1 shows the recorded data during lateral and longitudinal cyclic swept for 100 s. The data were filtered using a low pass filter at a cut-off frequency 15 Hz to remove the undesirable structural vibrations effect. Using the collected data, the suitable regression vector (network structure) and hidden neurons size were determined using the Lipschitz coefficient and k-fold cross validation technique previously discussed. To avoid over-fitting problem, the adaptability of the NN weights during training was reduced using the regularisation method. 5.2.1 Improving Generalisation of Neural Network through Regularisation The NN training used in this research work employed regularisation term in the error criterion to improve generalisation performance of the NN model. For a relatively small sized network model, a trial and error approach was adopted to select the appropriate weight decay parameter α to minimise the generalisation error. Figure 5.2 shows the effect of varying weight decay values on the training error, validation error and training iteration using attitude dynamic data set (y = [p q] T ; y = [δ lon δ lat ] T ). The result was obtained using the NN model trained with Levenberg-Marquardt (LM) algorithm using 5 hidden neurons and 8 regressors (n y = 3 and n u = 1). As the value of weight
5.2 OFF-LINE BASED SYSTEM IDENTIFICATION FOR MLP NETWORK 121 1 1 Pitch Angle (rad) 0.5 0 −0.5 Roll Angle (rad) 0.5 0 −0.5 −1 1.8 2 2.2 2.4 2.6 time (samples) x 10 4 −1 1.8 2 2.2 2.4 2.6 time (samples) x 10 4 2 2 Pitch Rate, q (rad/s) 1 0 −1 Roll Rate. p (rad/s) 1 0 −1 −2 1.8 2 2.2 2.4 2.6 time (samples) x 10 4 −2 1.8 2 2.2 2.4 2.6 time (samples) x 10 4 2 2 1 1 A x (m/s 2 ) 0 −1 A y (m/s 2 ) 0 −1 −2 1.8 2 2.2 2.4 2.6 time (samples) x 10 4 (a) −2 1.8 2 2.2 2.4 2.6 time (samples) x 10 4 1 1 Longitudinal Cyclic 0.5 0 −0.5 Lateral Cyclic 0.5 0 −0.5 −1 1.8 2 2.2 2.4 2.6 x 10 4 −1 1.8 2 2.2 2.4 2.6 x 10 4 (b) Figure 5.1 Sample of measurement data records during a longitudinal and lateral cyclic swept experiment: (a) The longitudinal (pitch angle θ, pitch rate, q and body acceleration in x-axis, A x) and lateral (roll angle φ, roll rate, p and body acceleration in y-axis, A y) output plots; and (b) The frequency swept plots of longitudinal cyclic δ lon and lateral cyclic δ lat .
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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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170 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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