Dual-Vector-Based Model Predictive Current Control with Online Parameter Identification for Permanent-Magnet Synchronous Motor Drives in Marine Electric Power Propulsion System

Due to its high efficiency, reliability, and environmental benefits, the permanent-magnet synchronous motor (PMSM) is increasingly being used in marine propulsion applications. As a promising solution, finite-control-set model predictive current control (FCS-MPCC) has been gaining attention in marine propulsion systems. However, FCS-MPCC for PMSM drives applies only a single switching state within each control cycle. Moreover, its prediction model depends on motor parameters. To address this issue, a dual-vector (DV)-based MPCC (DV-MPCC) incorporating online parameter identification was proposed. Firstly, a DV-MPCC suitable for a two-phase stationary reference frame was introduced. To reduce torque ripple, the DV combination was generated based on the error current vector, and the action time was allocated in accordance with the minimum root mean square error of the current. Furthermore, a model reference adaptive system (MRAS) for multi-parameter identification was developed based on the incremental current state equation. This equation was constructed and used as an adjustable model, enabling accurate estimation of resistance and inductance parameters, even when the flux parameter was completely unknown. Additionally, the proposed method addressed the identification error caused by rank deficiency. Experimental validation confirmed the effectiveness of the proposed method.