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

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.

• model-free predictive current control
• synchronous reluctance motor
• grey wolf optimizer
• parameter uncertainty
• nonlinear magnetic characteristics
• motor drives

• Sensorless Control of Electric Motors
• Multilevel Inverters and Converters
• Advanced Control Systems Optimization
• model-free predictive current control
• synchronous reluctance motor
• grey wolf optimizer
• parameter uncertainty
• nonlinear magnetic characteristics
• motor drives
• Electrical and Electronic Engineering
• Control and Systems Engineering
• Engineering