MRAC of nonlinear systems using neural networks with recursive least squares adaptation

Abstract

We present a new model reference adaptive control of nonlinear systems using neural networks. In this scheme, adaptive control is viewed as an identification process, in which the parameters to be identified are that of the controller and the plant model. The neural net controller is adapted using a variant of recursive least squares estimation which can be considered a generalization of back-propagation. Simulations show that for a simple plant, the adaptive control is stable.