Precision Position Control of PMSM using Neural Network Disturbance Observer and Parameter Compensator

Abstract

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer. To reduce the noise effect, the post-filter implemented by MA (moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feedforward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the parameter variation. A stability and usefulness are verified by computer simulation and experiment