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研究生:曾才榮
研究生(外文):Tsai-Jung Tseng
論文名稱:即時適應性類神經控制在非線性多輸入多輸出系統上之追蹤
論文名稱(外文):On-line Adaptive Neural Control for MIMO Nonlinear Systems Tracking Using Diagonal Recurrent Neural Network model
指導教授:李祖添李祖添引用關係
指導教授(外文):Tsu-Tian Lee
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:53
中文關鍵詞:適應性類神經控制器非線性
外文關鍵詞:adaptiveneuralcontroldiagonalrecurrentnonlinear
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本篇論文提出一種應用遞迴神經網路模型為學習基礎之非線性多輸入多輸出系統之即時適應性類神經滑動增益追蹤控制理論。使用非線性狀態回授來設計控制輸入項,其中包含由對角遞迴神經網路即時估測系統的多個未知參數來建立模型。這個使用滑動更新模式設計導出的適應性學習法則,可以在短時間內調整遞迴神經網路的權值到最佳模型狀態。模型系統的誤差會漸近收斂至一個小的區間 ,且滑動模式控制補償了此類神經近似的誤差。只要所有的滑動面達到一個小的範圍,則反饋式滑動增益的調整就會停止,此舉也可以避免不必要的參數漂移現象發生。本篇論文也證明了導出的閉迴路系統是穩定的,而多輸入多輸出非線性系統軌跡的追蹤也可完成。最後,也將此即時適應性遞迴神經網路控制器與其他適應性修正型倒傳遞神經網路控制器作比較,可得到較好的結果。

A approach for on-line adaptive neural control of MIMO nonlinear systems is explored in this thesis with sliding gain using diagonal recurrent neural network (DRNN) learning model. We use the nonlinear state feedback to design the control input of a nonlinear system containing several unknown parameters of nonlinear systems. These parameters are estimated by diagonal recurrent neural networks with on-line modeling. The resulting adaptive learning law, which is designed by sliding mode update, can adjust the weights of DRNN to the optimal value for modeling in short time. System modeling errors are asympotatically converged to the small region , and a sliding-mode control which compensates for the neural approximation errors is proposed. The adaptation of the feedback sliding gain will stop as soon as all sliding surface have reached a small range. As a result, the undesirable parameter drift phenomenon can be avoided. It is proved that the resulting close-loop system is stable and the trajectory tracking of MIMO nonlinear systems is achieved. Some simulation results are also provided to evaluate the design. Finally, simulation results show that on-line adaptive DRNN controller provide better performance than the other adaptive modify back-propagation neural controller.

CONTENTS
摘要 I
ABSTRACT II
CONTENTS III
LIST OF FIGURES IV
LIST OF TABLES VI
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Motivation 1
1.3 Organization of the thesis 3
Chapter 2 Nonlinear plants and Identification 4
2.1 Nonlinear MIMO plant 6
2.2 Diagonal Recurrent Neural Network Identification 7
Chapter 3 Robust Adaptive Neural Controller 14
3.1 Introduction 14
3.2 Controller Design 14
3.3 Diagonal Recurrent Neural Network Model 15
3.4 Robust Adaptive Controller Design 19
Chapter 4 Simulations 23
4.1 Tracking Control 25
4.2 Simulation with the adaptive modify back-propagation neural controller 37
Chapter 5 Conclusions 45
Appendix 46
References 50

[1] R. Baratti, B. Cannas, A. Fanni and F. Pilo, “Automated Recurrent Neural Network Design to Model the Dynamics of Complex Systems,” Neural Comput. & Applic. , 9, pp. 190-201, 2000
[2] K. S. Narendra and K. Parthasarathy, “Identification and Control of Dynamical Systems Using Neural Networks,” IEEE Transactions on Neural Networks, Vol. 1, NO. 1, pp. 4-27, March 1990
[3] E. B. Kosmatopoulos, P. A. Ioannou, and M. A. Christodoulou, “Identification of Nonlinear Systems Using New Dynamic Neural Network Structures,” IEEE Proceedings of the 31st Conference on Decision and Control, pp. 20-25, December 1992
[4] D. Gong and Y. Zhou, “Adaptive Control of Multi-Variables Nonlinear System based on Artificial Neural Network,” IEEE International Symposium on Industrial Electronics, Pusan, Korea, pp. 64-66, 2001
[5] C. Gan and K. Danai, “Model-based Recurrent Neural Network for Modeling Nonlinear Dynamic Systems,” IEEE Transactions on Systems, Man, and Cybernetics─Part B:Cybernetics, Vol. 30, No. 2, pp. 344-351, April 2000
[6] P. A. Ioannou and J. Sun, Robust Adaptive Control. Prentice Hall,1996
[7] X. cui and K. G. Shin, “Application of Neural Networks to Temperature Control in Thermal Power Plants,” Engineering applications of artificial Intelligence, Vol. 5, No. 6, pp. 527-538,1992
[8] M. M. Abdelhameed, “Adaptive Neural Network Based Controller for Robots,” Mechatronics, 9, pp.147-162, 1999
[9] G. Tao and P. V. Kokotovic, Adaptive Control of Systems with Actuator and Sensor Nonlinearities, Wiley 1996
[10] G. L. Plett. Adaptive Inverse Control of Plants with Disturbances. PhD thesis, Stanford University, CA, May 1998.
[11] J. Sum, C. S. Leung, G. H. Young, and W. Kan, “On the Kalman Filtering Method in Neural-Network Training and Pruning,” IEEE Transactions on Neural Networks, Vol. 10, No. 1, pp.161-166, January 1999
[12] S. Nakamura, Numerical Analysis and Graphic Visualization with MATLAB, Prentice-Hall, 1996
[13] G. A. Rovithakis and M. A. Christodoulou, Adaptive Control With Recurrent High-Order Neural Networks Theory and Industrial Applications. Springer 2000
[14] E. B. Kosmatopoulos, M. A. Christodoulou and P. A. Ioannou, “Dynamical Neural Networks that Ensure Exponential Identification Error Convergence,” Neural Networks, Vol. 10, No. 2, pp. 299-314, 1997
[15] A. G. Chassikos, H. A. Pak, P. A. Ioannou, “Nonlinear Robust Adaptive Control of A SCARA Manipulator,” Proceedings of the American Control Conference, pp1469-1475, 1989
[16] W. Kao, R. Horowitz, M. Tomizuka and M. Boals, “Repetitive Control of a Two Degree of Freedom SCARA Manipulator,” Proceedings of the American Control Conference, 1989
[17] D. L. Smith, “An Application of LQ Control to a Two Link SCARA Manipulator,” Proceedings of the American Control Conference, 1989
[18] J. Craig, Introduction to robotics: Mechanics and control. Reading, MA: Addison-Wesley, 1989
[19] D. T. Pham, and S. Yildirim, “Control of the Trajectory of a Planar Robot using Recurrent Hybrid Networks,” International Journal of Machine Tools & Manufacture, Vol. 39, pp. 415-429, 1999
[20] O. Barambones and V. Etxebarria, “An adaptive neural control scheme for mechanical manipulators with guaranteed stability,” Proceedings of the IEEE, pp357-362, 1999
[21] C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. Neural Networks, Vol. 6, pp. 144-156, Jan. 1995
[22] J. S. Cho, Y. W. Kim and D. J. Park, “Identification of Nonlinear Dynamic Systems Using High Order Diagonal Recurrent Neural Network,” Electronics Letters, 4th, Vol. 33, No. 25, pp. 2133-2135, December 1997
[23] S. S. Ge and C. Wang, “Direct Adaptive NN Control of a Class of Nonlinear Systems,” IEEE Trans. Neural Networks, Vol. 13, No. 1, January 2002
[24] Y. Li, N. Sundararajan, and P. Saratchandran, “Neuro-controller Design for nonlinear fighter aircraft maneuver using fully tuned RBF networks,” automatica, 37, pp. 1293-1301, 2001
[25] H. D. Patino and D. Liu, “Neural Network-Based Model Reference Adaptive Control System,” IEEE Transaction on System, Man ,and Cybernetics-Part B: Cybernetics, Vol. 30, No. 1, pp. 198-204, February 2000
[26] M. S. Ahmed, “Neural Net based MRAC for a class of nonlinear plants,” Neural Networks, Vol. 13, pp.111-124, 2000
[27] A. S. Poznyak, W. Yu, E. N. Sanchez, and J. P. Perez, “Nonlinear Adaptive Trajectory Tracking Using Dynamic Neural Networks,” IEEE Transactions on Neural Networks, Vol. 10, No. 6, pp. 1402-1411, November 1999
[28] E. N. Sanchez and M. A. Bernal, “Adaptive Recurrent Neural Control for Nonlinear System Tracking,” IEEE Transactions on System, Man, and Cybernetics-Part B: Cybernetics, Vol. 30, No. 6, pp. 886-889, December 2000
[29] W. S. Chow, X. D. Li, and Y. Fang, “A Real-Time Learning Control Approach for Nonlinear Continuous-Time System Using Recurrent Neural Networks,” IEEE Transactions on Industrial Electronics, Vol. 47, No.2, pp. 478-486, April 2000
[30] S. S. Ge, C. C. Hang, and L. C. Woon, “Adaptive Neural Network Control of Robot Maniplators in Task Space,” IEEE Transactions On Industrial Electronics, Vol. 44, No. 6, pp. 746-752, December 1997
[31] F. C. Sun, Z. Q. Sun, and Y. B. Chen, “Neural Adaptive Tracking Controller for Robot Manipulators with Unknown Dynamics,” IEE Proc.-Control Theory Appl., Vol. 147, No. 3, pp. 366-370, May 2000
[32] Y. G. Leu, W. Y. Wang, and T. T. Lee, “Robust Adaptive Fuzzy-Neural Controllers for Uncertain Nonlinear Systems,” IEEE Transaction on Robotics and Automation, Vol. 15, No. 5, pp. 805-817, October 1999
[33] T. Zhang, S. S. Ge and C. C. Hang, “Adaptive Neural Network Control for Strict-Feedback Nonlinear System Using Backstepping Design” Proceedings of American Control Conference San Diego, California, pp.1062-1066, June 1999
[34] H. H. Tack, C. G. Kim, M. G. Kim, and M. W. Jung, “Design of Globally Stable Robust Adaptive Neural Networks Controller for The Nonlinear System,” Fourth of International Conference on knowledge-Based Intelligent Engineering Systems & Allied Technologies, IEEE, pp. 410-413, Sep. 2000
[35] A. S. Poznyak and L. Ljung, “On-line Identification and Adaptive Trajectory Tracking for Nonlinear Stochastic Continuous Time Systems Using Differential Neural Networks,” Automatica, Vol. 37, pp.1257-1268, 2001
[36] F. Sun, Z. Sun, and P. Y. Woo, “Neural Network-Based Adaptive Controller Design of Robotic Manipulators with an Observer,” IEEE Transactions on Neural Networks, Vol. 12, No. 1, pp. 54-67, January 2001
[37] M. B. McFarland, and A. J. Calise, “Adaptive Nonlinear Control of Agile Antiair Missiles Using Neural Networks” IEEE Transaction on Control Systems Technology, Vol. 8, No. 5, pp. 749-756, September 2000
[38] J. O. Jang, and G. J. Jeon, “A Parallel Neuro-Controller for DC Motors Containing Nonlinear Friction,” NeuroComputing, Vol. 30, pp. 233-248, 2000
[39] A. Rubaai, and M. D. Kankam, “Adaptive Tracking Controller for Induction Motor Drives Using Online Training of Neural Networks,” IEEE Transactions on Industry Applications, Vol. 36, No. 5, pp. 1285-1294, September/October 2000
[40] J. O. Jang, “Implementation of Indirect Neuro-Control for a Nonlinear Two-Robot MIMO System,” Control Engineering Practice, Vol. 9, pp. 89-95, 2001
[41] J. A. Bullinaria, and P. M. Riddell, “Neural Network Control Systems That Learn to Perform Appropriately,” International Journal of Neural Systems, Vol. 11, No. 1, pp. 79-88, 2001
[42] F. Kamran, R. G. harley, B. Burton, and T. G. habetler, “An On-Line Trained Neural Network With An Adaptive Learning Rate for A Wide Range of Power Electronic Applications,” IEEE Proceedings, pp.1499-1505, 1996
[43] D. W. Ruck, S. K. Rogers, M. Kabrisky, P. S. Maybeck, and M. E. Oxley, “Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 6, pp. 686-691, June 1992
[44] G. V. Puskorius and F. A. Lee, “Neurocontrol of Nonlinear Dynamical Systems with Kalman Filter Trained Recurrent Networks,” IEEE Transactions on Neural Networks, Vol. 5, No. 2, pp.279-297, March 1994
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