SPEED SENSORLESS SLIDING MODE CONTROL FOR DUAL STAR INDUCTION MOTOR BASED ON HYBRID PSO-GWO ALGORITHM
DOI:
https://doi.org/10.59277/RRST-EE.2026.2.1Keywords:
Sliding mode controller, Dual star induction motor, Particle swarm grey wolf optimization (PSO-GWO), Model reference adaptive systemAbstract
This paper presents a sliding-mode model-reference adaptive system (SM-MRAS) observer for speed estimation in a sensorless, indirect vector control dual-star induction motor (DSIM) system. Firstly, a reference model of the DSIM rotor fluxes is provided using a voltage-model observer. The adjustable model adjusts the rotor flux estimates to match the reference model by adapting the speed, especially the speed estimate, which is a sliding-mode term. Secondly, the optimal parameters of the sliding mode controllers are optimized by the hybrid particle swarm grey wolf optimization (PSOGWO) algorithm. The main objective of this study is to demonstrate the effectiveness and validity of the proposed SMC-PSOGWO strategy. A comparative analysis with SMC-GWO and the conventional PI controller illustrates that the proposed method achieves a faster dynamic response and superior overall performance.
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