Precision Position Control of PMSM Using Neural Network Disturbance Observer on Forced Nominal Plant

This paper presents a neural network (NN) torque observer that is used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. Therefore, the response of PMSM (permanent magnet synchronous machine) follows that of the nominal plant. The load torque compensation method is composed of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM (recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper