基于模糊排序的永磁同步电动机模型预测电流控制

Predictive Current Control of Permanent Magnet Synchronous Motor Model Based on Fuzzy Sorting

针对永磁同步电动机模型预测电流控制的成本函数中权重系数不容易确定的问题,提出一种基于模糊排序法的模型预测电流控制策略。首先,通过排序法对不同电压矢量所对应的电流误差及切换时的开关频率进行排序,从而无须再对权重系数进行设计。由于控制目标重要性并不完全等同,设计一种带有变增益系数的排序优化方法,调节控制目标的重要程度。传统的权重系数有着连续的取值范围,而变增益系数的取值范围有着离散性和分段性的特点,相比较之下,后者的调整和设计过程更加的简易。再通过模糊控制,对变增益系数进行动态优化,从而更好地适应电机不断变化的运行状态。最后,仿真和半实物实验结果证明了所提方法的有效性和可行性。

Aiming at the problem of difficult design of cost function weight coefficients in model predictive current control of permanent magnet synchronous motor,a model predictive current control strategy based on fuzzy sorting method is proposed.First,the current errors and the switching frequency corresponding to different voltage vectors during the switching are sorted by the sorting method,thus eliminating the need to design the weigh coefficients.Since the control objectives are not exactly equal in importance,a sorting optimization method with variable gain coefficients is designed to adjust the importance of the control objectives by using the variable gain coefficients.Compared with the conventional weight coefficients,the variable gain coefficients have discrete segmentation characteristics,so their adjustment process can be effectively simplified.The variable gain coefficients are then dynamically optimized by means of fuzzy control to better adapt to the ever-changing operating conditions of the motor.Finally,the validity and feasibility of the method proposed in this paper is demonstrated by simulation and experimental results.