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.

This paper reports the optimal speed control of a permanent magnet synchronous motor (PMSM) system. The predictive control method is an effective strategy for a fast dynamic response. The undesirable performance in the presence of system disturbances, including internal model uncertainties and external load disturbances, is analyzed. To achieve a fast response and ensure stronger robustness and improved disturbance rejection performance simultaneously, a robust generalized predictive controller (GPC) with a high-order terminal sliding-mode observer (HOTSMO) is proposed for a PMSM control system. The proposed observer can estimate the unknown disturbances with chattering elimination. A feed-forward compensation based on the estimated disturbances is provided to the predictive speed controller. The simulation and experimental results show that the proposed control method can achieve a better speed dynamic response and a stronger robustness.

This paper reports on the optimal speed control problem in permanent magnet synchronous motor (PMSM) systems. To improve the speed control performance of a PMSM system, a model predictive control (MPC) method is incorporated into the control design of the speed loop. The control performance of the conventional MPC for PMSM systems is destroyed because of system disturbances such as parameter mismatches and external disturbances. To implement the MPC method in practical applications and to improve its robustness, a compensated scheme with an extended sliding mode observer (ESMO) is proposed in this paper. Firstly, for observing if and when the system model is mismatched, the ESMO is regarded as an extended sliding mode parameter observer (ESMPO) to identify the main mechanical parameters. The accurately obtained mechanical parameters are then updated into the MPC model. In addition, to overcome the influence of external load disturbances on the system, the observer is regarded as an extended sliding mode disturbance observer (ESMDO) to observe the unknown disturbances and provide a feed-forward compensation item based on the estimated disturbances to the model predictive speed controller. The simulation and experimental results show that the proposed ESMO can accurately observe the mechanical parameters of the system. Moreover, the optimized MPC improves the dynamic response behavior and exhibits a satisfactory disturbance rejection performance.

This paper concentrates on a robust resonant control strategy of a permanent magnet synchronous motor (PMSM) for electric drivers with model uncertainties and external disturbances to improve the control performance of the current loop. Firstly, to reduce the torque ripple of PMSM, the resonant controller with fractional order (FO) calculus is introduced. Then, a robust two degrees-of-freedom (Robust-TDOF) control strategy was designed based on the modified resonant controller. Finally, by combining the two control methods, this study proposes an enhanced Robust-TDOF regulation method, named as the robust two degrees-of-freedom resonant controller (Robust-TDOFR), to guarantee the robustness of model uncertainty and to further improve the performance with minimized periodic torque ripples. Meanwhile, a tuning method was constructed followed by stability and robust stability analysis. Furthermore, the proposed Robust-TDOFR control method was applied in the current loop of a PMSM to suppress the periodic current harmonics caused by non-ideal factors of inverter and current measurement errors. Finally, simulations and experiments were performed to validate our control strategy. The simulation and experimental results showed that the THDs (total harmonic distortion) of phase current decreased to a level of 0.69% and 5.79% in the two testing environments.

Permanent magnet synchronous motors (PMSMs) have attracted great attention in the field of electric drive system. However, the disturbances caused by parameter mismatching, model uncertainty, external load and torque ripple seriously weaken the control accuracy. The traditional adaptive sliding mode control (ASMC) methodology can address slow-varying uncertainties/disturbances whose frequencies are located at the bandwidth of the filter used to design the adaptive law well; however, it has been barely discussed with respect to the periodic situation. In this paper, we extend the ASMC arrangement to periodic case to suppress the torque ripple by using a series-structure resonant controller. Firstly, a typical SMC is designed to force the tracking error of speed to converge to zero and obtain a certain capacity to disturbance. Then, the improved adaptive law is incorporated to estimate the lumped disturbance and torque ripple. The improved adaptive law is enhanced by embedding the resonant controller, which can obtain a better estimating result for torque ripple with repetitive feature. Finally, simulation and experimental results with PI, SMC and proposed methods are compared to verify the effectiveness of the developed controller.

The sCMOS imaging system with deep vacuum cooling technology has become a necessary way to improve the detection capability of space targets. In order to improve the detection capability of the photoelectric detection equipment for space targets, this paper developed the Highly Sensitive Deep Vacuum Cooling Imaging System (HSDVCIS). Firstly, we designed the imaging readout processing circuit using the GSENSE4040 sCMOS image sensor designed and manufactured by Gpixel and the deep vacuum cooling structure using thermoelectric cooling. Then, we tested the designed HSDVCIS with readout noise, dark current, and dynamic range of 3.96 $e^{-}$, 0.12 $e^{-}$/pixel/sec, and 84.49 dB, respectively, and tested the image sensor with a minimum cooling temperature of −40 °C. Finally, according to the results of observation experiments, we validated that the photoelectric detection equipment equipped with HSDVCIS improved the limiting detection magnitude (at SNR = 5 level) from 13.22 to 13.51 magnitudes within a 3 s exposure time by turning on the cooling function. Therefore, HSDVCIS designed in this paper can achieve highly sensitive detection of space targets. At the same time, the development of HSDVCIS also provides technical reserves and strong support for future research on the imaging systems using multiple image sensor mosaics.