【目的】为解决双三相永磁同步电机(PMSM)模型预测控制(MPC)系统鲁棒性低、对电机参数依赖性高的问题，对滑模参数辨识方法进行改进优化，提出了一种基于超螺旋滑模观测器(ST-SMO)参数辨识的双三相PMSM模型预测电流控制(MPCC)方法。【方法】首先，引入高阶滑模算法，替代传统滑模观测器中的开关函数sign作为新的滑模趋近律，设计了基于超螺旋算法的ST-SMO来进行更加精准的电机电感参数辨识；然后，利用Lyapunov理论对设计的高阶滑模观测器进行稳定性分析，并结合增量式方程对MPCC系统中的预测模型进行优化，消除了预测方程中磁链参数对电机控制系统鲁棒性的影响；最后，结合参数辨识算法对电感参数进行准确辨识，降低了MPCC系统对电机参数的依赖性。【结果】改进后的双三相PMSM MPCC系统，在加入了ST-SMO和增量式预测模型后，具有良好的稳态和动态性能。在参数辨识方面，不仅消除了一阶滑模系统带来的抖振现象，提升了参数辨识的精度，还提高了参数辨识速度，保证双三相PMSM在参数失配时，电机控制系统仍能保持较好的控制性能。【结论】本文所提基于ST-SMO参数辨识的MPCC系统在转速和转矩突变等多种工况下具有良好的可行性和稳定性。

[Objective] To address the issues of low robustness and high dependence on motor parameters in model predictive control (MPC) systems for dual three-phase permanent magnet synchronous motor (PMSM), this study improves and optimizes the sliding mode parameter identification method. A dual three-phase PMSM model predictive current control (MPCC) method based on super-twisting sliding mode observer (ST-SMO) parameter identification is proposed. [Methods] Firstly, a higher-order sliding mode algorithm was introduced to replace the switching function sign in traditional sliding mode observers as the new sliding mode reaching law. A super-twisting algorithm-based ST-SMO was designed to achieve more accurate identification of motor inductance parameters. Then, stability analysis of the designed higher-order sliding mode observer was conducted using Lyapunov theory. Combined with incremental equations, the prediction model in the MPCC system was optimized, eliminating the effect of flux linkage parameters on the robustness of the motor control system. Finally, the inductance parameters were accurately identified using the parameter identification algorithm, reducing the dependence of the MPCC system on motor parameters. [Results] The improved dual three-phase PMSM MPCC system incorporating the ST-SMO and incremental prediction model demonstrated excellent steady-state and dynamic performance. For parameter identification, it eliminated the chattering phenomenon caused by the first-order sliding mode system, improved the accuracy of parameter identification, and enhanced the identification speed of motor parameters. Additionally, the control system maintained high control performance for the dual three-phase PMSM under parameter mismatch conditions. [Conclusion] The MPCC system based on ST-SMO parameter identification proposed in this study demonstrates good feasibility and stability under various operating conditions, such as speed and torque sudden transients.

国家自然科学基金项目(61603263)