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In the permanent magnet synchronous motor (PMSM) speed control system, having a rapidly and steady current control internal loop is the sticking point to guaranteeing that the motor has good output torque performance. The deadbeat predictive current control (DPCC) strategy has rapidly current response, but it depends heavily on the precise mathematical model of the machine. When the motor parameter in the current loop controller has a mismatch with real system, the steady error of the output current of motor will appear, and even lead to system oscillations. Therefore, a robust predicted current control of PMSM based on novel sliding model observer is proposed in this article. First, the performance of the traditional DPCC when the machine mathematical model mismatch is analyzed, and then a sliding mode observer based on an exponential-based bi-power reaching law is proposed, which can predict stator current and estimate the current transient at same time. Compared with the traditional DPCC, the scheme of this paper can remove stator current static error caused by parameter mismatch. The validity and effectiveness of method are proved by experiment.

For rejecting aperiodic disturbances at the moment of loading, following the periodic velocity reference signals better, and realizing the precise velocity tracking control in Permanent Magnet Synchronous Linear Motors (PMLSMs), an extended state observer based repetitive control strategy (ESO-RC) is proposed in this paper. First, a repetitive controller is used to track periodic signal, and suppress the periodic disturbances caused by slot effect. Then, low pass filter, dynamic compensator, and time-delay compensation are added to the repetitive controller to enhance the tracking ability and dynamic ability. Second, an ESO method is taken to improve the velocity tracking ability and anti-disturbance performance, because it can compensate the aperiodic disturbances, and alleviate the velocity drop amplitude especially at the moment of loading. Last, the performance of the ESO-RC strategy is verified. The velocity tracking curves and thrust force curves at different velocity are figured out and analyzed in Matlab.

This paper proposes a novel method to estimate the moment of inertia for permanent magnet synchronous motors (PMSMs) control system. It is based on unscented Kalman filter (UKF). Compared with the conventional method extended Kalman filter(EKF), UKF approximates the probability density distribution of a nonlinear function, rather than using first-order linearization method to approximate. The calculation accuracy of nonlinear distribution statistic based on UKF is able to reach second-order at least, and the validity of the proposed algorithm is proved by computer simulations compared with the conventional method.

Recently, model predictive torque control (MPTC) has received more and more attention in the field of induction motor (IM) drives due to its fast torque response. However, in the traditional MPTC method, the torque reference in the cost function is generated by the proportional-integral (PI) controller, thus the torque reference's generation rate is not fast when the load torque is given suddenly. This paper investigates a model predictive torque control based on a disturbance observer for induction motor drives. The disturbance observer is used as the feedforward compensation part for the PI controller, and it not only improves the torque reference's rate, but enhances the system load disturbance rejection ability. The simulation results demonstrate the correctness and the effectiveness of the proposed method.

A novel speed and flux estimation method of permanent magnet synchronous motor(PMSM) for sensorless vector control based on Robust Extended Kalman Filter(REKF) is proposed in this paper. The effect of gross error on estimation accuracy of Extended Kalman Filter (EKF) is analyzed. The model of PMSM based on REKF is established, and whether REKF can give better estimation performance than EKF is discussed when PMSM is disturbed by gross error. The speed estimation error and the flux change coursed by external gross error and internal estimation gross error based on REKF are compared with EKF. REKF has better performance against gross error than EKF, and the system can converge faster when disturbed. The simulation results verify the validity of the proposed speed and flux estimation method based on Robust Extended Kalman Filter.

A novel speed sensorless vector control of induction motor based on robust extended Kalman filter(REKF) is proposed in this paper. Whether REKF can give better estimation performance and robust ability than EKF for speed and flux estimation of induction machines is explored. The speed estimated error and the flux fluctuation of REKF by gross external disturbance and internal estimated error is compared with EKF. The simulation results show that REKF has the better estimation performance of anti-gross error than EKF.