Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69164
Title: Adaptive control for a class of nonlinear discrete-time systems using neural networks
Authors: Ge, S.S. 
Li, G.Y.
Lee, T.H. 
Issue Date: 2001
Source: Ge, S.S.,Li, G.Y.,Lee, T.H. (2001). Adaptive control for a class of nonlinear discrete-time systems using neural networks. IEEE International Symposium on Intelligent Control - Proceedings : 97-102. ScholarBank@NUS Repository.
Abstract: In this paper, the adaptive control problem is studied for a class of discrete-time unknown nonlinear systems with general relative degree in the presence of bounded disturbances. To derive the feedback control, a causal state-space model of the plant is obtained. By using an NN observer to estimate the unavailable but predictable states of the system, a Lyapunov-based adaptive state feedback NN controller is proposed. The state feedback control avoids the possible singularity problem in adaptive nonlinear control. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). An arbitrarily small tracking error can be achieved if the size of neural networks is chosen large enough, and the control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.
Source Title: IEEE International Symposium on Intelligent Control - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/69164
Appears in Collections:Staff Publications

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