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Stable adaptive neurocontrol for nonlinear discrete-time systems

Zhu, Quanmin; Guo, Lingzhong


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Quan Zhu
Professor in Control Systems

Lingzhong Guo


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 are provided to demonstrate the efficiency of the approach.


Zhu, Q., & Guo, L. (2004). Stable adaptive neurocontrol for nonlinear discrete-time systems. IEEE Transactions on Neural Networks, 15(3), 653-662.

Journal Article Type Article
Publication Date May 1, 2004
Journal IEEE Transactions on Neural Networks
Print ISSN 1045-9227
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 15
Issue 3
Pages 653-662
Keywords stable adaptive neurocontrol, nonlinear discrete-time systems
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Additional Information Additional Information : The concise combination of adaptation, neural computation, linear control techniques, and system stabilisation form a significant methodological package for addressing the problem of controlling complex nonlinear dynamics.