Intelligent control using neural networks and multiple models

In this paper a new framework for intelligent control is established to adaptively control a class of nonlinear discrete time dynamical systems while assuring boundedness of all signals. A linear robust adaptive controller and multiple nonlinear neural network based adaptive controllers are used, and a switching law is suitably defined to switch between them, based upon their performance in predicting the plant output. Boundedness of all the signals is established regardless of the parameter adjustment mechanism of the neural network controllers, and thus neural network models can be used in novel ways to better detect changes in the system and provide starting points for adaptation. The effectiveness of the proposed approach is demonstrated by simulation studies.

[1]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[2]  K. Astrom.  Where is the intelligence in intelligent control? , 1991, IEEE Control Systems.

[3]  Karl Johan Åström,et al.  Where is the intelligence in intelligent control , 1991 .

[4]  Snehasis Mukhopadhyay,et al.  Intelligent Control Using Neural Networks , 1991, 1991 American Control Conference.

[5]  G. Wittum,et al.  Adaptive filtering , 1997 .

[6]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[7]  Frank L. Lewis,et al.  High-Level Feedback Control with Neural Networks , 1998, World Scientific Series in Robotics and Intelligent Systems.

[8]  Kumpati S. Narendra,et al.  Issues in the application of neural networks for tracking based on inverse control , 1999, IEEE Trans. Autom. Control..

[9]  T. Parisini,et al.  Hybrid control based on discrete-event automata and receding-horizon neural controllers , 2000, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[10]  Kumpati S. Narendra,et al.  Adaptive control of discrete-time systems using multiple models , 2000, IEEE Trans. Autom. Control..

[11]  Hybrid control based on discrete-event automata and receding-horizon neural controllers , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[12]  Elena R. Messina,et al.  The challenge of intelligent systems , 2000, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[13]  Marios M. Polycarpou.  Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach , 2001, IEEE Trans. Autom. Control..

[14]  Kumpati S. Narendra,et al.  Nonlinear adaptive control using neural networks and multiple models , 2001, Autom..

[15]  Yixin Diao,et al.  Intelligent fault tolerant control using adaptive schemes and multiple models , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[16]  K. Narendra,et al.  Identification and control of a nonlinear dynamical system based on its linearization. II , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).