A Robust Super Twisting Controller Algorithm for Dual Star Induction Motor Based on Particle Swarm Optimization Algorithm

Abstract

This research introduces an optimized control approach for a dual star induction motor (DSIM) using a combination of the Super Twisting Algorithm (STA) and Particle Swarm Optimization Algorithm (PSO) within the framework of indirect field-oriented control (IFOC) and two pulse width modulation (PWM) voltage sources. The primary aim of this study is to reduce torque fluctuations, minimize variations in stator current, and enhance the precision of speed control. The DSIM consists of two sets of three-phase windings with separate neutrals and a 30-degree electrical phase shift. The utilization of the STA control method addresses issues related to response time, steady-state error, and the impact of load disturbances, ultimately improving speed control. The Simulation conducted in MATLAB/SIMULINK are used to compare the performance of the proposed STA-PSO controller against a conventional Proportional-Integral (PI) controller. The outcomes demonstrate the substantial enhancements achieved by the STA-PSO controller in speed control, including reduced response time, improved steady-state error, and increased resilience against external disturbances. The proposed STA-PSO control strategy effectively mitigates torque and stator currents fluctuations in DSIM’s while offering superior speed control performance. These findings underscore the potential of the STA controller for enhancing control in various DSIM applications.