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Dynamic recurrent neural network for system identification and control

Delgado, A., Kambhampati, C. and Warwick, K. (1995) Dynamic recurrent neural network for system identification and control. IEE Proceedings-Control Theory and Applications, 142 (4). pp. 307-314. ISSN 1350-2379

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To link to this article DOI: 10.1049/ip-cta:19951873

Abstract/Summary

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:17870
Publisher:IET

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