Neural Network Control of Unknown Nonlinear Systems

Abstract

A method for using neural networks to control unknown dynamic systems is presented. The method can be regarded as an adaptive controller for time-invariant nonlinear systems with completely unknown dynamics, except for the system order. A backpropagation neural network is used to model the unknown nonlinear system on-line, based on a functional representation relating plant inputs to plant outputs. The same neural network is used to generate the control signals given the measurements of the current states and the desired values of future states. Although a backpropagation network with supervised learning is used, the training is based on measurement data obatined during the system operation, so that there is no need for an outside "teacher" telling the neural controller about the correct control signals.