A DIRECT ADAPTIVE NEURAL-NETWORK CONTROL FOR UNKNOWN NONLINEAR-SYSTEMS AND ITS APPLICATION

In this paper a direct adaptive neural-network control strategy for un known nonlinear systems is presented. The system considered Is describ ed by an unknown NARMA model and a feedforward neural network is used to learn the system. Taking the neural network as a neuro model of the system, control signals are directly obtained by minimizing either th e instant difference or the cumulative differences between a setpoint and the output of the neuro model. Since the training algorithm guaran tees that the output of the neuro model approaches that of the actual system, it is shown that the control signals obtained can also make th e real system output close to the setpoint, An application to a flow-r ate control system is included to demonstrate the applicability of the proposed method and desired results are obtained.