基于模糊决策的永磁同步电机驱动的两矢量无量纲模型预测控制

Nabil Farah;Gang Lei;Jianguo Zhu;Youguang Guo
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引用次数: 0

摘要

模型预测控制(MPCs)具有非线性多变量控制的优点,比其他常用的永磁同步电机(PMSM)驱动控制方法具有更好的性能。然而,传统MPC具有各种问题,包括不令人满意的稳态性能、可变的开关频率以及难以选择适当的加权因子。本文提出了两种不同的改进MPC方法来处理这些问题。一种方法是双矢量无量纲模型预测转矩控制(MPTC)。采用两个成本函数(转矩和流量)和模糊决策来消除加权因子,并选择第一个最优矢量。转矩成本函数选择占空比基于转矩误差确定的第二矢量。另一种方法是双矢量无量纲模型预测电流控制(MPCC)。第一矢量的选择与传统MPC方法中相同。使用两个独立的电流成本函数和模糊决策来选择第二个矢量,该矢量的占空比是基于电流误差确定的。两种提出的方法都利用空间矢量PWM调制器来调节开关频率。数值模拟结果表明,与传统MPC和其他改进MPC相比,该方法具有更好的稳态和瞬态性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-vector Dimensionless Model Predictive Control of PMSM Drives Based on Fuzzy Decision Making
Model predictive controls (MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor (PMSM) drives. However, the conventional MPCs have various issues, including unsatisfactory steady-state performance, variable switching frequency, and difficult selection of appropriate weighting factors. This paper proposes two different improved MPC methods to deal with these issues. One method is the two-vector dimensionless model predictive torque control (MPTC). Two cost functions (torque and flux) and fuzzy decision-making are used to eliminate the weighting factor and select the first optimum vector. The torque cost function selects a second vector whose duty cycle is determined based on the torque error. The other method is the two-vector dimensionless model predictive current control (MPCC). The first vector is selected the same as in the conventional MPC method. Two separate current cost functions and fuzzy decision-making are used to select the second vector whose duty cycle is determined based on the current error. Both proposed methods utilize the space vector PWM modulator to regulate the switching frequency. Numerical simulation results show that the proposed methods have better steady-state and transient performances than the conventional MPCs and other existing improved MPCs.
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