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一种改进的双三相永磁同步电机无价值函数模型预测转矩控制OACSTPCD

An Improved Model Predictive Torque Control for DTP-PMSM Without Cost Function

中文摘要英文摘要

针对双三相永磁同步电机(DTP-PMSM)传统模型预测转矩控制策略转矩性能较差的问题,提出了一种基于虚拟电压矢量的无价值函数模型预测转矩控制策略.首先,对矢量空间解耦(VSD)的数学建模方法进行研究,建立了基于VSD的DTP-PMSM数学模型;其次,构建了一组12个无需矢量替换的虚拟电压矢量,通过无差拍直接转矩和磁链控制预测出参考矢量的角度位置和幅值信息,并筛选出最优虚拟矢量,该方法避免了虚拟电压矢量的遍历寻优从而大幅减小计算负担;同时,通过一种简化的矢量幅值调整方法使该最优虚拟矢量的幅值大小无限逼近参考矢量幅值大小,有效降低转矩脉动;最后,搭建了试验测试平台,将所提策略与传统策略进行对比.结果表明,所提策略有效减小了谐波电流和转矩脉动.

Aiming at the problem of poor torque performance of dual three-phase permanent magnet synchronous motor(DTP-PMSM)traditional model predictive torque control strategy,a model predictive torque control without cost function strategy based on virtual voltage vectors is proposed.Firstly,the mathematical modeling method of vector space decomposition(VSD)is investigated,and the DTP-PMSM mathematical model is establish based on VSD.Secondly,a set of 12 virtual voltage vectors without vector replacement are constructed,and the angular position and magnitude information of the reference vector are predicted by dead-beat direct torque and flux control,and the optimal virtual vector with the angular position closest to the reference vector is filtered out,which avoids traversal of virtual voltage vectors to seek for the optimal and thus reduces the computational burden significantly.And at the same time,a simplified vector magnitude adjustment method is used to make the magnitude of the optimal vector infinitely close to the magnitude of the reference vector,which effectively reduces the torque pulsation.Finally,an experimental test platform is built to compare the proposed strategy with the traditional strategy.The results show that the proposed strategy effectively reduces the current harmonics and torque pulsations.

张平化;范慧妍;王爽

凡己科技(苏州)有限公司,江苏苏州 215000上海大学机电工程与自动化学院,上海 200444

动力与电气工程

双三相永磁同步电机模型预测转矩控制虚拟电压矢量转矩脉动

dual three-phase permanent magnet synchronous motormodel predictive torque controlvirtual voltage vectortorque pulsation

《电机与控制应用》 2024 (005)

30-38 / 9

上海市自然科学基金(19ZR1418600)Natural Science Foundation of Shanghai,China(19ZR1418600)

10.12177/emca.2024.023

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