Optimisation of Model Predictive Torque Control Strategy with Standard and Multi-Objective Genetic Algorithms

Zerdali, Emrah; Gurel, Aycan

doi:10.2478/pead-2023-0020

Abstract

In this paper, the flux error-related weighting factor (WF) of the predictive torque control (PTC) strategy for induction motor (IM) control is optimised by a standard genetic algorithm (SGA) through speed errors only and multi-objective genetic algorithm (MOGA) through torque and flux errors. This paper compares the performances of both optimisation methods. Compared to MOGA, SGA offers a straightforward way to select WF and does not need a decision-making method to choose a final solution. But MOGA considers the given problem in a multi-objective way and directly optimises the control objectives of the PTC strate-gy. Comparisons are made over the flux and torque ripples, total harmonic distortion of stator phase current, and average switching frequency for different operating conditions. Simulation results show that both methods choose a close WF value. Consequently, SGA stands out in the optimisation of the PTC strategy with its simple structure.

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Optimisation of Model Predictive Torque Control Strategy with Standard and Multi-Objective Genetic Algorithms

Abstract

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