Dynamic recurrent neural network for system identification and controlDelgado, 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1049/ip-cta:19951873 Abstract/SummaryA 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.
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