Please use this identifier to cite or link to this item: https://doi.org/10.1080/0020717031000073063
Title: Adaptive NN control for a class of discrete-time non-linear systems
Authors: Ge, S.S. 
Lee, T.H. 
Li, G.Y.
Zhang, J. 
Issue Date: 10-Mar-2003
Source: Ge, S.S., Lee, T.H., Li, G.Y., Zhang, J. (2003-03-10). Adaptive NN control for a class of discrete-time non-linear systems. International Journal of Control 76 (4) : 334-354. ScholarBank@NUS Repository. https://doi.org/10.1080/0020717031000073063
Abstract: In this paper, adaptive neural network (NN) control is investigated for a class of single-input single-output (SISO) discrete-time unknown non-linear systems with general relative degree in the presence of bounded disturbances. Firstly, the systems are transformed into a causal state space description, adaptive state feedback NN control is presented based on Lyapunov's stability theory. Then, by converting the systems into a causal input-output representation, adaptive output feedback NN control is given. Finally, adaptive NN observer design and observer-based adaptive control are presented under the assumption of persistent excitation (PE). All the control schemes avoid the so-called controller singularity problem in adaptive control. By suitably choosing the design parameters, the closed-loop systems are proven to be semi-globally uniformly ultimately bounded (SGUUB). Simulation studies show the effectiveness of the newly proposed schemes.
Source Title: International Journal of Control
URI: http://scholarbank.nus.edu.sg/handle/10635/54923
ISSN: 00207179
DOI: 10.1080/0020717031000073063
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