Abstract
Some of the problems that arise in the control of nonlinear systems in the presence of uncertainty are considered. Multilayer neural networks and radial basis function networks are used in the design of identifiers and controllers, and gradient methods are used to adjust their parameters. For a restricted class of nonlinear systems, it is shown that globally stable adaptive controllers can be determined. Simulation results are presented to demonstrate that the methods presented can be used for the effective control of complex nonlinear systems. >
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