Output tracking of a class of unknown nonlinear discrete-time systems using neural networks

In this paper, an adaptive controller based on neural networks is derived for controlling a class of unknown nonlinear discrete-time systems. A two-layered neural network is used to characterize the input-output behavior of the unknown systems. The Widrow-Hoff delta rule is the learning algorithm us...

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Bibliographic Details
Published in:Journal of the Franklin Institute 1998-04, Vol.335 (3), p.503-515
Main Author: Horng, Jui-Hong
Format: Article
Language:English
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Summary:In this paper, an adaptive controller based on neural networks is derived for controlling a class of unknown nonlinear discrete-time systems. A two-layered neural network is used to characterize the input-output behavior of the unknown systems. The Widrow-Hoff delta rule is the learning algorithm used to minimize the error signal between the actual response and that of the neural networks. The control signal is generated on-line using another two-layered neural network, so that the plant results in zero asymptotic tracking errors with respect to a desired reference signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stability. The effectiveness of the proposed control scheme is also demonstrated by a simulation example.
ISSN:0016-0032
1879-2693
DOI:10.1016/S0016-0032(96)00131-7