【目的】为解决永磁同步电机(PMSM)模型预测电流控制(MPCC)参数依赖性强的问题,建立PMSM单步和两步无模型预测电流控制(MFPCC)系统。【方法】基于超局部模型实现PMSM单步和两步MFPCC,采用微分代数法确定一阶和二阶超局部模型中未知部分的估计值,分析超局部模型的窗口序列长度对控制性能的影响及参数变化下基于超局部模型的PMSM单步和两步MFPCC的参数鲁棒性,并进行实时性试验验证。【结果】仿真与实时性试验结果表明:一阶超局部模型的窗口序列长度对控制性能影响较大,随着窗口序列长度的增大,控制性能提升,并趋于饱和。二阶超局部模型的窗口序列长度对性能影响较小。参数变化下,基于超局部模型的PMSM单步和两步MFPCC具有较好的参数鲁棒性。随着窗口序列长度的增大,超局部模型计算耗时轻微增大,但对整体实时性影响较小。【结论】基于超局部模型的PMSM单步和两步MFPCC可行,参数鲁棒性强。基于超局部模型的单步MFPCC和传统单步MPCC的实时性基本相当。基于超局部模型的两步MFPCC的实时性略优于传统两步MPCC。

[Objective] One-step and two-step model-free predictive current control (MFPCC) for permanent magnet synchronous motor (PMSM) system is established to address the strong parameter dependence issue in model predictive current control (MPCC). [Method] Based on the ultralocal model, one-step and two-step MFPCC for PMSM was implemented. The differential algebraic method was used to estimate uncertain parts of the first-order and second-order ultralocal models. The effect of the window sequence length of the ultralocal model on control performance was analyzed, along with the parameter robustness of the ultralocal model-based one-step and two-step MFPCC for PMSM with parameter variations. Real-time experiments were conducted to verify the results. [Results] Simulation and real-time experimental results showed that the window sequence length of the first-order ultralocal model significantly impacted the control performance. Increasing the window sequence length improved control performance until saturation. The window sequence length of the second-order ultralocal model had a smaller effect on control performance. The ultralocal model-based one-step and two-step MFPCC for PMSM showed strong parameter robustness with parameter variations. With the increase in window sequence length, the computational time for the ultralocal model increased slightly, but the overall real-time performance was minimally affected. [Conclusion] The ultralocal model-based one-step and two-step MFPCC for PMSM is feasible and demonstrates strong parameter robustness. The real-time performance of the ultralocal model-based one-step MFPCC is comparable to that of the conventional one-step MPCC. The ultralocal model-based two-step MFPCC exhibits slightly better real-time performance than the conventional two-step MPCC.

长安大学2024年研究生科研创新实践项目(300103724044,300103724045)