The Development of Neural Network Based System Identification ...

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220 REFERENCES B. Mettler, Mark B. Tischler, and T. Kanade. System identification of a model-scale helicopter. Technical report, Robotics Institute, Carnegie Mellon University, 2000. B. Mettler, C. Dever, and E. Feron. Identification modeling, flying qualities, and dynamic scaling of miniature rotorcraft. In NATO System Concepts and Integration (SCI) Symposium, Berlin, May 2002a. Bernard Mettler. Identification modeling and characteristics of miniature rotorcraft. Kluwer Academic Publishers, Boston, 1st edition, 2003. Bernard Mettler, Mark B. Tischler, and Takeo Kanade. System identification modeling of a small-scale unmanned rotorcraft for flight control design. Journal of the American Helicopter Society, 47(1):50–63, 2002b. A. Naghdinezhad, M. Mohamadian, and A. Dastfan. Comparison of off line neural network training methods for sensorless induction motor drive. In Universities Power Engineering Conference, 2006. UPEC ’06. Proceedings of the 41st International, volume 2, pages 709 –713, Sept. 2006. doi: 10.1109/UPEC.2006.367571. H. Nakanishi and K. Inoue. Development of autonomous flight control systems for unmanned helicopter by use of neural networks. In Neural Networks, 2002. IJCNN ’02. Proceedings of the 2002 International Joint Conference on, volume 3, pages 2626 –2631, 2002. doi: 10.1109/IJCNN.2002.1007558. H. Nakanishi, H. Hashimoto, N. Hosokawa, A. Sato, and K. Inoue. Autonomous flight control system for unmanned helicopter using neural networks. In SICE 2002. Proceedings of the 41st SICE Annual Conference, volume 2, pages 777 – 782 vol.2, aug. 2002. doi: 10.1109/SICE.2002.1195255. K.S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. Neural Networks, IEEE Transactions on, 1(1):4 –27, mar 1990. ISSN 1045-9227. doi: 10.1109/72.80202. L. S. H. Ngia and J. Sjoberg. Efficient training of neural nets for nonlinear adaptive filtering using a recursive levenberg-marquardt algorithm. IEEE Transactions on Signal Processing, 48(7):1915–1927, 2000. David Nodland, Arpita Ghosh, H. Zargarzadeh, and S. Jagannathan. Neurooptimal control of an unmanned helicopter. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2012. doi: 10.1177/ 1548512912450369. URL http://dms.sagepub.com/content/early/2012/07/15/ 1548512912450369.abstract. K. Nonami. Autonomous flying robots: unmanned aerial vehicles and micro aerial vehicles. Springer, New York, 2010. ISBN 4431538550, 9784431538561, 4431538569, 9784431538554. Kenzo Nonami, Farid Kendoul, Satoshi Suzuki, Wei Wang, and Daisuke Nakazawa. Linearization and identification of helicopter model for hierarchical control design. In Autonomous Flying Robots, pages 95–130. Springer Japan, 2010. ISBN 978-4- 431-53855-4. doi: 10.1007/978-4-431-53856-1 5. URL http://dx.doi.org/10.1007/ 978-4-431-53856-1_5.
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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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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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Page 229 and 230: 7.3 EXPERIMENTAL RESULTS 197 indica
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Page 233 and 234: 7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
Page 235 and 236: 7.3 EXPERIMENTAL RESULTS 203 mainta
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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 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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