Adaptive control using neural networks and approximate models
- 1 May 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 8 (3) , 475-485
- https://doi.org/10.1109/72.572089
Abstract
The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.Keywords
This publication has 8 references indexed in Scilit:
- Adaptive control of nonlinear multivariable systems using neural networksNeural Networks, 1994
- Control of nonlinear dynamical systems using neural networks: controllability and stabilizationIEEE Transactions on Neural Networks, 1993
- Gradient methods for the optimization of dynamical systems containing neural networksIEEE Transactions on Neural Networks, 1991
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- Nonlinear Dynamical Control SystemsPublished by Springer Nature ,1990
- Backpropagation through time: what it does and how to do itProceedings of the IEEE, 1990
- A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksNeural Computation, 1989
- Input-output parametric models for non-linear systems Part I: deterministic non-linear systemsInternational Journal of Control, 1985