Nonlinear control via generalized feedback linearization using neural networks

Abstract

A novel approach to nonlinear control, called Generalized Feedback Linearization (GFL), is presented. This new strategy overcomes one important drawback of the well known Feedback Linearization strategy, in the sense that it is able to handle a broader class of nonlinear systems, namely those having unstable zero dynamics. It is shown that the use of a nonlinear predictor for the system output is a key feature in the derivation of the control strategy. For certain types of systems this predictor can be found as a nonlinear function of the system input and output, allowing an output feedback control solution. The use of Artificial Neural Networks (ANN) to directly parameterize the predictor of the controlled variable when an explicit model for the system is not available, is investigated via computer simulations. This approach is based on the functional approximation capability of multi layer ANN.