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Stable receding horizon control based on recurrent networks

Kambhampati, C., Delgado, A., Mason, J. D. and Warwick, K. (1997) Stable receding horizon control based on recurrent networks. IEE Proceedings-Control Theory and Applications, 144 (3). pp. 249-254. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:19970950

Abstract/Summary

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18048
Uncontrolled Keywords:nonlinear system control, recurrent neural networks, stable receding horizon control
Publisher:IET

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