This article presents grey relational analysis (GRA)-based objective function optimization in predictive torque control (PTC) for induction machine. Selection of appropriate weighting factor in the objective function is one of the key aspects in the implementation of PTC. However, selection of suitable weighting factor in the objective function is a heuristic task. To address this issue, GRA method is implemented for the objective function optimization. In this approach, single-objective function is modified into two independent objective functions for stator flux and torque. A grey relational grade is used to identify the suitable control action in each sampling. A MATLAB/Simulink model is developed to validate the control algorithm under various operating conditions of the drive, and corresponding results are compared with experimental results.

中文翻译：

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基于灰色关联分析的感应电机转矩预测控制目标函数优化

本文介绍了基于灰色关联分析 (GRA) 的感应电机预测转矩控制 (PTC) 目标函数优化。在目标函数中选择合适的权重因子是实施PTC的关键环节之一。然而，在目标函数中选择合适的加权因子是一项启发式任务。为了解决这个问题，GRA 方法被用于目标函数优化。在这种方法中，单目标函数被修改为两个独立的定子磁通和转矩目标函数。灰色关联等级用于识别每个采样中的合适控制行为。开发了MATLAB/Simulink 模型来验证驱动器在各种运行条件下的控制算法，并将相应的结果与实验结果进行比较。