【目的】为了降低永磁同步电机(PMSM)最大转矩电流比控制(MTPA)对电机参数的依赖,本文建立基于虚拟信号注入的永磁同步电机增量式模型预测电流控制MTPA,并采用模型参考自适应系统(MRAS)实现参数辨识,提高参数鲁棒性。【方法】采用传统公式法实现MTPA,其需要定子dq轴电感和转子磁链3个参数,但这3个参数失配对MTPA计算结果影响均较大。建立永磁同步电机增量式模型预测电流控制系统,其需要定子dq轴电感和定子电阻3个参数,其中定子电阻参数失配影响较小。采用虚拟注入法实现MTPA,其需要定子d轴电感和定子电阻2个参数,其中定子d轴电感影响较大,定子电阻失配影响较小。综上,建立基于虚拟信号注入的永磁同步电机增量式模型预测电流控制MTPA,并采用模型参考自适应系统对定子dq轴电感进行辨识,将辨识参数用于增量式模型预测电流控制和虚拟信号注入法MTPA,以提高参数鲁棒性。基于Matlab/Simulink建立仿真模型,对上述方法进行仿真分析。【结果】基于虚拟信号注入法MTPA的增量式模型预测电流控制采用MRAS仅需辨识dq轴电感这2个参数。采用MRAS参数辨识,在参数匹配和失配情况下,虚拟信号注入法MTPA计算得到的电流矢量角βMTPA误差较小,定子电阻参数失配影响较小。【结论】虚拟信号注入法MTPA仅需定子d轴电感和定子电阻这两个参数,参数依赖性小,且定子电阻失配影响较小。基于虚拟信号注入的永磁同步电机增量式模型预测电流控制MTPA仅需辨识dq轴电感。采用MRAS辨识参数的虚拟信号注入法MTPA的误差较小,提升了参数鲁棒性。

[Objective] In order to reduce the dependence of maximum torque per ampere (MTPA) control of permanent magnet synchronous motor (PMSM) on motor parameters, the incremental model predictive current control (MPCC) MTPA for PMSM based on virtual signal injection is established in this paper. And model reference adaptive system (MRAS) is used to identify parameters to improve parameters robustness. [Methods] The traditional formula method was adopted to achieve MTPA, which required three parameters—stator dq-axis inductance and rotor flux. However, mismatches in these three parameters all had a large impact on the MTPA calculation results. An incremental MPCC for PMSM was established, which required three parameters—stator dq-axis inductance and stator resistance. Among them, the mismatch in stator resistance had a relatively small impact. The virtual signal injection method was adopted to achieve MTPA, which required two parameters—stator d-axis inductance and stator resistance. Among them, the mismatch in stator d-axis inductance had a large impact, while the mismatch in stator resistance had a small impact. In summary, an MTPA based on virtual signal injection combined with incremental MPCC for PMSM was established, and the MRAS was adopted to identify the stator dq-axis inductances. The identified parameters were used in both the incremental model predictive current control and the virtual signal injection MTPA to improve parameter robustness. A simulation model was built based on Matlab/Simulink, and the above methods were simulated and analyzed. [Results] The incremental MPCC for PMSM based on the virtual signal injection MTPA adopted MRAS to identify dq-axis stator inductance. Under both matched and mismatched parameter conditions, the error of the current vector angle βMTPA calculated by the virtual signal injection MTPA was small, and the impact of stator resistance mismatch was small. [Conclusion] The virtual signal injection MTPA only requires d-axis stator inductance and stator resistance, has low parameter dependency, and the impact of stator resistance mismatch is small. The incremental MPCC MTPA for PMSM based on virtual signal injection requires only the identification of the dq-axis inductances. The virtual signal injection MTPA using MRAS for parameter identification has a small error, which improves parameter robustness.

青海职业技术大学青海省高原汽车电动化与智能化技术重点实验室开放基金（QZDSZ03-202502）