Abstract
Models for the identification and control of nonlinear dynamical systems using neural networks were introduced by Narendra and Parthasarathy in 1990, and methods for the adjustment of model parameters were also suggested. Simulation results of simple nonlinear systems were presented to demonstrate the feasibility of the schemes proposed. The concepts introduced at that time are investigated in this paper in greater detail. In particular, a number of questions that arise when the methods are applied to more complex systems are addressed. These include nonlinear systems of higher order as well as multivariable systems. The effect of using simpler models for both identification and control are discussed, and a new controller structure containing a linear part in addition to a multilayer neural network is introduced.
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