Global robust stabilizing control for a dynamic neural network system

Authors:
Ziqian Liu;Stephen C. Shih;Qunjing Wang
Affiliations:
Engineering Department, State University of New York, Maritime College, Throggs Neck, NY;School of Information Systems and Applied Technologies, Southern Illinois University Carbondale, Carbondale, IL;Department of Electrical Engineering, Hefei University of Technology, Hefei, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2009

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Abstract

This paper presents a new approach for the global robust stabilizing control of a class of dynamic neural network systems. This approach is developed via Lyapunov stability and inverse optimality, which circumvents the task of solving a Hamilton-Jacobi-Isaacs equation. The primary contribution of this paper is the development of a nonlinear H∞ control design for a class of dynamic neural network systems, which are usually used in the modeling and control of nonlinear affine systems with unknown nonlinearities. The proposed H∞ control design achieves global inverse optimality with respect to some meaningful cost functional, global disturbance attenuation, and global asymptotic stability provided that no disturbance occurs. Finally, four numerical examples are used to demonstrate the effectiveness of the proposed approach.