Grey Relational Analysis-Based Objective Function Optimization for Predictive Torque Control of Induction Machine

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.

Automated summary

This article introduces the objective function optimization in predictive torque control for induction machine using grey relational analysis (GRA). The GRA method is utilized to modify the single-objective function into two independent objective functions for stator flux and torque, identifying suitable control actions. A MATLAB/Simulink model is developed to validate the control algorithm under various operating conditions and compared with experimental results.