Global exponential stability of recurrent neural networks for synthesizing linear feedback control systems via pole assignment

Authors:
Yunong Zhang;Jun Wang
Affiliations:
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin;-
Venue:
IEEE Transactions on Neural Networks
Year:
2002

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Abstract

Global exponential stability is the most desirable stability property of recurrent neural networks. The paper presents new results for recurrent neural networks applied to online computation of feedback gains of linear time-invariant multivariable systems via pole assignment. The theoretical analysis focuses on the global exponential stability, convergence rates, and selection of design parameters. The theoretical results are further substantiated by simulation results conducted for synthesizing linear feedback control systems with different specifications and design requirements