基于新型解析权重因子配置的感应电机模型预测转矩控制

doi:10.19595/j.cnki.1000-6753.tces.221465

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摘要在感应电机有限集预测转矩控制中,权重因子的配置是多控制目标协同优化的关键参数。由于缺乏理论指导,通常采用试凑法,因而难以实现电驱系统的最优控制。针对此问题,本文提出了基于新型解析权重配置的感应电机预测转矩控制。首先,分析了不同转速转矩工况下,权重因子对电磁转矩脉动、定子磁链幅值脉动和电流谐波含量的影响机理。然后依据感应电机电磁关系的内在联系,解析地推导了权重因子配置的数学表达式,可实现不同工况下感应电机的多目标最优控制。鉴于权重因子解析表达式依赖于电机参数,本文融合了在线参数辨识技术,可实现权重因子及预测模型的计算精度。实验结果验证了所提算法的有效性。

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	颜黎明
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关键词 ：模型预测控制, 感应电机, 预测转矩控制, 权重因子, 代价函数

Abstract：Finite control set-model predictive control (FCS-MPC) has received extensive attention in the field of electric drive systems in recent years, because of its intuitive concept, fast dynamic response, easy to deal with nonlinear and multivariable constraints. Especially, finite control set-model predictive torque control (FCS-MPTC), which uses electromagnetic torque and stator flux as control variables, has the advantages of fast transient response of electromagnetic torque, easy to handle field weakening control. However, the weighting factor allocation of the FCS-MPTC cost function has always been a research difficulty in the academic community. Since the essence of FCS-MPTC is the exhaustive method of discrete voltage vectors, the setting of weight factor also lacks theoretical basis. Rating method and cut-and-trial method are two classical weighting factor design strategies. The rating method sets the weighting factor as the ratio of rated electromagnetic torque to rated stator flux amplitude. This method can realize the coordinated control of electromagnetic torque and stator flux under rated condition of IM, but the control performance is poor under other conditions. The cut-and-trial method obtains the weighting factor configuration through repeated online debugging, which is not only cumbersome, but also difficult to obtain optimal control.
Firstly, the influence mechanism of different weighting factors on stator current total harmonic distortion (THD) of stator current, root mean square (RMS) of electromagnetic torque and RMS of stator flux linkage is studied. The research results show that the stator flux RMS decreases with the increase of the weighting factor, while the electromagnetic torque RMS is just the opposite, especially the stator current THD decreases first and then increases. Secondly, Stator current THD, RMS of electromagnetic torque and RMS of stator flux linkage of the motor under different operating conditions with a fixed weighting factor are studied. The results show that when the induction motor (IM) FCS-MPTC operates at low speed, the performances of stator flux RMS and electromagnetic torque RMS become worse. For the induction motor FCS-MPTC under low speed and light load, the stator current THD presents large harmonics. Then, the mathematical expression of weighting factor is derived analytically according to the internal relations of electromagnetic relations of IM, which can achieve multi-objective optimal control of induction motor under different working conditions. Because the analytical expression of the weighting factor depends on the motor parameters, this paper integrates the online parameter identification technology, which can achieve the calculation accuracy of the weighting factor and the prediction model. The proposed analytical weighting factor-model predictive torque control (AWF-MPTC) is compared with traditional methods, including dynamic performance, steady performance and robust performance. The experimental results show that AWF-MPTC has smaller stator current THD and electromagnetic torque ripple than traditional MPTC (T-MPTC). In steady state, stator current THD of AWF-MPTC is 11.52%, while that of T-MPTC is 16.13%. T-MPTC stator flux ripple is 0.12 Wb, while AWF-MPTC is 0.068 Wb. At the same time, AWF-MPTC has better robustness than T-MPTC. There are two main reasons for the improvement of robustness performance. First, the analytical weighting factor calculation method realizes the coordinated control of electromagnetic torque and stator flux. Secondly, the online identification of induction motor parameters not only improves the accuracy of the prediction model, but also improves the accuracy of the weighting factor.
Compared with T-MPTC, the proposed AWF-MPTC has better dynamic performance, steady performance and robustness. Under different operating conditions, the optimal coordinated control of electromagnetic torque and stator flux is realized.

Key words： Model predictive control induction motor predictive torque control weighting factor cost function

PACS:

TM346

基金资助:国家自然科学基金（52107036）和中国博士后科学基金特别资助（2022T150071）资助项目

通讯作者: 颜黎明男,1988年生,讲师,硕士生导师,研究方向为电动汽车电机驱动和充电技术等。E-mail：ylm@chd.edu.cn

作者简介: 郭鑫男,1998年生,硕士研究生,研究方向电动汽车驱动电机的模型预测控制。E-mail：gxlocas@163.com;徐玺声男,1999年生,硕士研究生,研究方向为电动汽车驱动电机的模型预测控制。E-mail：xuxisheng1999@163.com;魏巍男,2001年生,硕士研究生,研究方向电动汽车充电技术。E-mail：weiwei152024@163.com

引用本文:

颜黎明, 郭鑫, 徐玺声, 魏巍. 基于新型解析权重因子配置的感应电机模型预测转矩控制[J]. 电工技术学报, 0, (): 62-62. Yan Liming, Guo Xin, Xu Xisheng, Weiwei. Model predictive torque control of induction machine drives using novel analytic weighting factor assignment. Transactions of China Electrotechnical Society, 0, (): 62-62.

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