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A multilayer recurrent neural network is proposed for on-line synthesis of minimum-norm linear feedback control systems through pole assignment. The proposed neural network approach uses a four-layer recurrent neural network for the on-line computation of feedback gain matrices with the minimum Frobenius norm and desired closed-loop poles. The proposed recurrent neural network is shown to be capable of synthesizing minimum-norm linear feedback control systems in real time. The operating characteristics of the recurrent neural network and feedback control systems are demonstrated by use of an illustrative example.
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, and University of North DakotaGrand Forks, NDU.S.A.