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Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

Zhu, Q. M., Ma, Z. and Warwick, K. (1999) Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems. IEE Proceedings-Control Theory and Applications, 146 (4). pp. 319-326. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:19990364

Abstract/Summary

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:17799
Uncontrolled Keywords:minimum variance control, self-tuning, nonlinear systems, discrete-time systems, neural network mapping, uncertain systems, recursive least squares, adaptive control, neurocontrol
Publisher:IET

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