Sliding mode control of nonlinear systems using Gaussian radial basis function neural networks

Abstract

A method for driving the dynamics of a nonlinear system to a sliding mode is discussed. The approach is based on a sliding mode control methodology, i.e., the system under control is driven towards a sliding mode by tuning the parameters of the controller. In this loop, the parameters of the controller are adjusted such that a zero learning error level is reached in one dimensional phase space defined on the output of the controller. A Gaussian radial basis function neural network is used as the controller.