Work

Model Free Predictive Current Control Based on a Grey Wolf Optimizer for Synchronous Reluctance Motors

Year: 2022

Type: article

Abstract: A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameters errors. In the case of the synchronous reluctance motor (SynRM), where the parameters vary due to its ferromagnetic structure and nonlinear magnetic properties, MBPCC performance would suffer significantly. Accordingly, in this paper, a Grey Wolf Optimizer based model-free predictive current control (GW-MFPCC) of SynRM is proposed, to skip all the effects of the model dependency and parameters uncertainty. The proposed method predicts the stator current through tracking the minimum cost function using the grey wolf optimizer. The proposed GW-MFPCC scheme is compared to MBPCC, and its effectiveness is evaluated and confirmed by experimental results.

Source: Electronics

DOI: 10.3390/electronics11244166

Authors: Abdelkader Mahmoudi, Imed Jlassi, Antonio J. Marques Cardoso, Khaled Yahia

Institutions: University of Beira Interior, University of Biskra, University of Eloued

Language: English

Topic: Sensorless Control of Electric Motors

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,094

Funders: Fundação para a Ciência e a Tecnologia, European Regional Development Fund