The Development of Neural Network Based System Identification ...

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222 REFERENCES V. R. Puttige and S. G. Anavatti. Real-time neural network based online identification technique for a uav platform. In Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on, pages 92–92, 2006. Vishwas Ramadas Puttige. Neural network based adaptive control for autonomous flight of fixed wing unmanned aerial vehicles. PhD thesis, School of Aerospace, Civil and Mechanical Engineering, Australian Defence Force Academy, University of New South Wales, August 2009. URL http://handle.unsw.edu.au/1959.4/43736. A. Rahideh, M.H. Shaheed, and H.J.C. Huijberts. Dynamic modelling of a trms using analytical and empirical approaches. Control Engineering Practice, 16(3):241 – 259, 2008. ISSN 0967-0661. J. B. Rawlings. Tutorial overview of model predictive control. Control Systems Magazine, IEEE, 20(3):38–52, 2000. M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: the rprop algorithm. In Neural Networks, 1993., IEEE International Conference on, volume 1, pages 586 –591, 1993. B.P. Rimal, I.E. Putro, A. Budiyono, D. Min, and E. Choi. System Identification of NN-based Model Reference Control of RUAV during Hover, chapter 19, pages 395–420. InTech, 2011. Roadmap. Unmanned aircraft systems roadmap, 2005-2030. Technical report, Office of the Secretary of Defense, Department of Defense, USA, 2005. J. A. Rossiter. Model-based predictive control : a practical approach. CRC Press, Boca Raton, 2003. S. Saarinen, R. Bramley, and G. Cybenko. Ill-conditioning in neural network training problems. SIAM J. Sci. Comput., 14(3):693–714, May 1993. ISSN 1064-8275. doi: 10.1137/0914044. URL http://dx.doi.org/10.1137/0914044. Balasaheb Patre Sadhana Chidrawar and Laxman Waghmare. Neural Generalized Predictive Control for Industrial Processes, chapter 12, pages 199–230. InTech, 2009. doi: 10.5772/7905. URL http: //www.intechopen.com/books/automation-control-theory-and-practice/ neural-generalized-predictive-control-for-industrial-processes. Mario E. Salgado, Graham C. Goodwin, and Richard H. Middleton. Modified least squares algorithm incorporating exponential resetting and forgetting. International Journal of Control, 47(2):477–491, 1988. URL http://www.tandfonline.com/doi/ abs/10.1080/00207178808906026. M. K. Samal, S. Anavatti, and M. Garratt. Real-time neural network based identification of a rotary-wing uav dynamics for autonomous flight. In Industrial Technology, 2009. ICIT 2009. IEEE International Conference on, pages 1–6, 2009. Mahendra Samal. Neural network based identification and control of an unmanned helicopter. Phd thesis, School of Engineering and Information Technology, University of New South Wales, 2009.
REFERENCES 223 Mahendra Kumar Samal, Sreenatha Anavatti, and Matthew Garratt. Neural network based system identification for autonomous flight of an eagle helicopter. In 17th World Congress The International Federation of Automatic Control, volume 17, pages 7421–7426, 2008. Mahendra Kumar Samal, Sreenatha Anavatti, Tapabrata Ray, and Matthew Garratt. A computationally efficient approach for nn based system identification of a rotary wing uav. International Journal of Control, Automation and Systems, 8:727–734, 2010. ISSN 1598-6446. doi: 10.1007/s12555-010-0403-5. URL http://dx.doi.org/ 10.1007/s12555-010-0403-5. Sandhya Samarasinghe. Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Auerbach Publications, Boca Raton, FL, 1 edition, 2007. ISBN 084933375X, 9780849333750. R. San Martin, A. Barrientos, P. Gutierrez, and J. del Cerro. Unmanned aerial vehicle (uav) modelling based on supervised neural networks. In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages 2497 –2502, May 2006. C. P. Sanders, P. A. DeBitetto, E. Feron, H. F. Vuong, and N. Leveson. Hierarchical control of small autonomous helicopters. In Decision and Control, 1998. Proceedings of the 37th IEEE Conference on, volume 4, pages 3629–3634, 1998. S. Saripalli, J. F. Montgomery, and G. S. Sukhatme. Visually guided landing of an unmanned aerial vehicle. Robotics and Automation, IEEE Transactions on, 19(3): 371–380, 2003. P.S. Sastry, G. Santharam, and K.P. Unnikrishnan. Memory neuron networks for identification and control of dynamical systems. Neural Networks, IEEE Transactions on, 5(2):306 –319, mar 1994. ISSN 1045-9227. doi: 10.1109/72.279193. R. Setiono, Wee Kheng Leow, and J.M. Zurada. Extraction of rules from artificial neural networks for nonlinear regression. Neural Networks, IEEE Transactions on, 13 (3):564 –577, May 2002. ISSN 1045-9227. doi: 10.1109/TNN.2002.1000125. M. Hasan Shaheed. Feedforward neural network based non-linear dynamic modelling of a trms using rprop algorithm. Aircraft Engineering and Aerospace Technology, 77(1): 13–22, 2005. M.H. Shaheed and M.O. Tokhi. Dynamic modelling of a single-link flexible manipulator: parametric and non-parametric approaches. Robotica, 20(01):93–109, 2002. Syariful S. Shamsudin and XiaoQi Chen. Identification of an unmanned helicopter system using optimised neural network structure. International Journal of Modelling, Identification and Control, 17(3):223–241, 2012a. Syariful Syafiq Shamsudin and XiaoQi Chen. Recursive gauss-newton based training algorithm for neural network modelling of an unmanned helicopter dynamics. In Mechatronics and Machine Vision in Practice (M2VIP), 2012 19th International Conference, pages 92–99, 2012b.
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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 233 and 234: 7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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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 256 and 257: 224 REFERENCES D.H. Shim. Hierarchi
Page 258 and 259: 226 REFERENCES Nikos Vitzilaios and
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