An approximate internal model-based neural control for unknown nonlinear discrete processes

An approximate internal model-based neural control (AIMNC) strategy is proposed for unknown nonaffine nonlinear discrete processes under disturbed environment. The proposed control strategy has some clear advantages in respect to existing neural internal model control methods. It can be used for open-loop unstable nonlinear processes or a class of systems with unstable zero dynamics. Based on a novel input-output approximation, the proposed neural control law can be derived directly and implemented straightforward for an unknown process. Only one neural network needs to be trained and control algorithm can be directly obtained from model identification without further training. The stability and robustness of a closed-loop system can be derived analytically. Extensive simulations demonstrate the superior performance of the proposed AIMNC strategy

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

[2]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[3]  Sylvie Galichet,et al.  Nonlinear internal model control: application of inverse model based fuzzy control , 2003, IEEE Trans. Fuzzy Syst..

[4]  M. Morari,et al.  Internal model control. VI: Extension to nonlinear systems , 1986 .

[5]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[6]  Kevin M. Passino,et al.  Stable Adaptive Control and Estimation for Nonlinear Systems , 2001 .

[7]  C. A. Desoer,et al.  Nonlinear Systems Analysis , 1978 .

[8]  Léon Personnaz,et al.  Nonlinear internal model control using neural networks: application to processes with delay and design issues , 2000, IEEE Trans. Neural Networks Learn. Syst..

[9]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[10]  Derong Liu,et al.  Neural networks for modeling and control of dynamic systems: a practitioner's handbook: M. Nørgaard, O. Ravn, N.K. Poulsen, and L.K. Hansen; Springer, London, 2000, 246pp., paperback, ISBN 1-85233-227-1 , 2002, Autom..

[11]  Dale E. Seborg,et al.  Nonlinear internal model control strategy for neural network models , 1992 .

[12]  Daniel Sbarbaro,et al.  Neural Networks for Nonlinear Internal Model Control , 1991 .

[13]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[14]  M. Morari,et al.  Internal Model Control: extension to nonlinear system , 1986 .

[15]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[16]  S. S. Ge,et al.  Adaptive NN control for a class of discrete-time non-linear systems , 2003 .

[17]  Snehasis Mukhopadhyay,et al.  Adaptive control using neural networks and approximate models , 1997, IEEE Trans. Neural Networks.

[18]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[19]  Kumpati S. Narendra,et al.  Gradient methods for the optimization of dynamical systems containing neural networks , 1991, IEEE Trans. Neural Networks.

[20]  George W. Irwin,et al.  Nonlinear control structures based on embedded neural system models , 1997, IEEE Trans. Neural Networks.

[21]  Han-Xiong Li,et al.  A robust disturbance-based control and its application , 1993 .

[22]  Snehasis Mukhopadhyay,et al.  Adaptive control of nonlinear multivariable systems using neural networks , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[23]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[24]  Liang Jin,et al.  Fast neural learning and control of discrete-time nonlinear systems , 1995, IEEE Trans. Syst. Man Cybern..