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
Full text not archived in this repository.
To link to this article DOI: 10.1049/ip-cta:19951873
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.
Repository Staff Only: item control page