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Permanent magnet synchronous motors (PMSMs) are widely used in the field of industrial servo control, especially in high-precision applications. Owing to the periodic torque ripple caused by the cogging torque, flux harmonics, and current offsets, the speed output of the system has a periodic ripple, which affects the control accuracy of the servo system. The conventional proportional-integral controllers cannot reject torque ripple and are highly dependent on motor parameters. This limits the control performance when a PMSM is used as a high-precision servo system. Thus, this paper proposes a combination of model predictive control (MPC) and iterative learning control (ILC) to not only speed up the response time of the system but also effectively reduce the speed ripples. MPC updates the predictive model in real time through feedback and evaluates the system output and control rate according to the cost function. It obtains an optimal control sequence for the next moment and has good parameter robustness and fast response. ILC records the speed of ripple signals over an entire cycle and then uses those signals to compensate for the control signal in the next cycle. It is capable of reducing the periodic speed ripples. The experimental verification of the schemes was conducted on a digital signal processor-field programmable gate-array-based platform. The experimental results obtained confirm the effectiveness of the proposed MPC-ILC scheme. INDEX TERMS Iterative learning control, model predictive control, PMSM control, speed ripple.

The sliding mode control (SMC) strategy is employed to a permanent magnet synchronous motor (PMSM) vector control system in this study to improve system robustness against parameter variations and load disturbance. To decrease the intrinsic chattering behavior of SMC, a speed SMC with an adaptive law and an extended state observer (ESO) is proposed. In this method, based on the Lyapunov stability theorem, adaptive estimation laws are deduced to estimate uncertainties of a PMSM caused by parameter variations and unmodeled dynamics. Online estimated uncertainties can be used to eliminate the effect caused by the real uncertainties. In addition, an ESO is applied to observe the load disturbance in real time. The load disturbance observed value is then utilized to the output side of the speed adaptive SMC controller as feed-forward compensation. Both the simulation and experiment results demonstrate that the proposed approach effectively alleviates system chattering and enhances system robustness against uncertainty and load disturbance.