Application of neural networks to nonlinear adaptive control systems

This paper presents a method of model reference adaptive control for nonlinear systems using neural networks. The control input is given by the sum of the output of a model reference adaptive controller and the output of the neural network. The neural network is used to compensate the nonlinearity of the plant dynamics that is not taken into consideration in the usual model reference adaptive control. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. New parallel neural networks are proposed.