基于 SPMSM 的半增量式无电机参数预测电流控制

Semi-Incremental Motor-Parameter-Free Predictive Current Control Based on Surface-Mounted Permanent Magnet Synchronous Motor

摘要

为了提高表贴式永磁同步电机无差拍预测电流控制对电机参数的鲁棒性，提出了一种半增量式的无电机参数预测电流控制策略．首先，针对传统预测控制策略对电机参数敏感的问题，通过分析 q、d 轴预测模型的不同特性，建立了 q 轴增量预测模型，有效消除了磁链参数对控制性能的影响；其次，考虑到直流母线电压传感器存在测量误差，通过获取仅与三相逆变器开关动作状态相关的归一化电压，对直流母线电压进行标幺化处理来改进直交轴预测电流模型；最后，在改进的 d、q 轴预测模型中引入表征集总扰动的电流调整因子和电压调整因子，从而得到了无电机参数的电流预测模型．电流调整因子包含了磁饱和、热电阻效应等与电流相关的集总扰动；电压调整因子包含了与直流母线电压有关的逆变器非线性、磁饱和等集总扰动．为实时调整模型中的电压调整因子和电流调整因子，本文采用带遗忘因子的最小二乘法进行在线参数辨识，该方法通过动态调整遗忘因子权重，有效平衡了系统对新数据的响应速度和对噪声的抑制能力，从而提升了电机的多参数鲁棒性．通过实验对比分析了所提出的控制策略与无差拍预测电流控制和基于超局部模型的预测电流控制策略，结果表明本文所提策略在电感和直流母线电压失配的情况下，电流控制效果良好，具有更强的鲁棒性．

Abstract

To enhance the parameter robustness of deadbeat predictive control in surface-mounted permanent magnet synchronous motors，a semi-incremental motor parameter-free predictive control strategy was proposed. First，a q-axis incremental predictive model was established to address the sensitivity of traditional predictive control strategy to motor parameters. By analyzing the different characteristics of the d-axis and q-axis predictive models，this model effectively eliminated the influence of flux parameters on the control performance. Next，considering the measurement errors inherent in direct current(DC)bus voltage sensors，a normalized voltage associated solely with the switching states of the three-phase inverter was obtained. On this basis，per-unit processing was applied to the DC-bus voltage to refine the predictive current models for the d- and q-axes. Finally，current- and voltage-adjustment factors that characterize lumped disturbances were incorporated into the improved d-axis and q-axis prediction models，yielding a motor-parameter-free current prediction model. The current-adjustment factors incorporate rent-dependent lumped disturbances，such as magnetic saturation and thermal-resistance effects，whereas the volt-age-adjustment factors encompass lumped disturbances associated with the DC-bus voltage，including inverter nonlinearity and magnetic saturation. To update the voltage and current-adjustment factors in the model in real time，a least squares method with a forgetting factor was adopted for online parameter identification. By dynamically adjusting the weight of the forgetting factor，the adopted method effectively balances the response speed of the system to new data with its ability to suppress noise，thereby improving the multiparameter robustness of the motor. Experimental comparisons are conducted among the proposed control method，deadbeat predictive current control，and ultralocal model-based predictive current control strategy. The results reveal that the proposed strategy exhibits the greatest robustness against mismatches in inductance and DC-bus voltage parameters.

关键词

表贴式永磁同步电机 / 无电机参数 / 最小二乘 / 鲁棒性

Key words

surface-mounted permanent magnet synchronous motor(SPMSM) / motor parameter-free / least squares / robustness

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基金资助

国家自然科学基金资助项目(52377065)