期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 15, 期 3, 页码 653-662出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2004.826131
关键词
adaptive control; nonlinear discrete-time system; recurrent neural networks
This paper presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of,controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network weight adaptation law is derived from Lyapunov stability analysis and the connection between convergence of the network Weight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability, of the closed-loop systems. Two simulation examples ate provided to demonstrate the efficiency of the approach.
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