【目的】针对永磁同步电机(PMSM)模型预测电流控制在电机参数失配工况下控制性能下降的问题，本文提出了一种在设计过程中无需使用电机参数的PMSM无模型控制策略。【方法】基于PMSM的超局部模型，设计了非线性扩张状态观测器(NESO)，并根据Lyapunov理论进行了稳定性分析，构建了基于NESO的无模型预测电流控制系统；同时分析了超局部模型中电流反馈增益对无模型控制性能的影响，基于采样电流迭代对电流反馈增益进行了在线辨识。使用Matlab/Simulink对算法进行了仿真研究，首先在系统的给定电流反馈增益系数为标称值的工况下，对本文提出的基于NESO的无模型控制系统进行了验证；然后在系统给定电流反馈增益参数失配的工况下，与传统方法进行了仿真对比；最后对基于采样电流迭代的电流反馈增益参数在线辨识方法进行了验证。【结果】仿真结果表明，在超局部模型中电流反馈增益参数失配时，NESO的输出比传统线性扩张状态观测器更稳定，基于NESO的PMSM控制系统的电流环跟踪效果更好，相电流中的谐波含量也得到了降低。在仿真持续给定系统失配的电流反馈增益参数时，基于采样电流迭代的在线参数辨识方法可以准确识别实际的电流反馈增益参数，快速收敛并保持稳定。【结论】基于NESO的PMSM控制策略与传统控制方法相比降低了系统的设计参数敏感度，在电机参数不匹配的工况下具有更高的控制性能。

[Objective] To address the issue that the control performance of model predictive current control for permanent magnet synchronous motor (PMSM) deteriorates under the working condition of motor parameter mismatch, this paper proposes a model-free control strategy for PMSM that does not require the use of motor parameters in the design process. [Method] Based on the ultra-local model of PMSM, a nonlinear extended state observer (NESO) was designed, and its stability was analyzed using Lyapunov theory. A model-free predictive current control system based on the NESO was constructed. Meanwhile, the influence of the current feedback gain in the ultra-local model on the model-free control performance was analyzed, and online identification of the current feedback gain was performed based on the sampled current iteration. The algorithm was simulated and studied using Matlab/Simulink. Firstly, under the working condition where the given current feedback gain coefficient of the system was the nominal value, the model-free control system based on the NESO proposed in this paper was verified. Then, under the working condition where the given current feedback gain parameter of the system was mismatched, a comparative simulation was conducted with the traditional methods. Finally, the online identification method of the current feedback gain parameter based on the sampled current iteration was verified. [Result] Simulation results show that when the current feedback gain parameters are mismatched in the ultra-local model, the output of the NESO is more stable than that of the traditional linear extended state observer. The current loop tracking effect of the PMSM control system based on the NESO is better, and the harmonic content in the phase current is also reduced. The online parameter identification method based on sampling current iteration can accurately identify the actual current feedback gain parameters, rapidly converge and maintain stability when the mismatch current feedback gain parameters are continuously applied in simulation. [Conclusion] Compared with the traditional control methods, the control strategy of PMSM based on the NESO reduces the sensitivity of the system’s design parameters and offers higher control performance under the working condition of motor parameter mismatch.

国家自然科学基金(61603263)