A direct adaptive neural network control for unknown nonlinear systems and its application

Abstract

Presents a direct adaptive neural network control strategy for unknown nonlinear systems which are described by an unknown NARMA model. Taking the neural network as a neuro model of the system, control signals are directly obtained by minimizing either the instant difference or the cummulative differences between a setpoint and the output of the neuro model. An application to a flow rate control system is studied and desired results are obtained.