Tuning the weighting factor is crucial to model predictive torque and flux control. A finite set of discrete weighting factors is utilized in this research to determine the optimum solution. The Pareto line optimization technique is implemented to prevent the occurrence of local optimum solutions. By conducting an accuracy analysis, the number of discrete weighting factors is optimized, and the number of iterations is reduced. The stator current distortion minimization criterion is used to obtain the ultimate global optimal solution from the Pareto line. This study compares the results of the proposed optimization method and the particle swarm optimization method based on experimental data from a 4 kW induction motor drive test bench. The proposed technique can achieve the global optimum weighting factor in a shorter computational duration while maintaining a slightly lower total harmonics distortion and torque ripple.

中文翻译：

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感应电机模型预测控制权重因子的离散优化

调整权重因子对于预测扭矩和磁通控制模型至关重要。本研究利用一组有限的离散权重因子来确定最佳解决方案。采用帕累托线优化技术来防止出现局部最优解。通过进行精度分析，优化了离散权重因子的数量，并减少了迭代次数。定子电流畸变最小化准则用于从帕累托线获得最终的全局最优解。本研究基于 4 kW 感应电机驱动试验台的实验数据，比较了所提出的优化方法和粒子群优化方法的结果。所提出的技术可以在更短的计算时间内实现全局最优权重因子，同时保持略低的总谐波失真和扭矩脉动。