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

A model reference adapative control of nonlinear systems using neural networks is presented. 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 backpropagation. Simulations show that, for a simple plant, adaptive control is stable