Model-Predictive Control of Open-End Winding Synchronous Reluctance Motor Drives

This paper presents an advanced control strategy for a Synchronous Reluctance Machine (SyRel) in an Open-End Winding (OEW) configuration. The goal is to enhance performance and control flexibility in dual-inverter systems, which present strong multivariable interactions and nonlinear dynamics. To address these challenges, a Finite-Control-Set Model-Predictive Control (FCS-MPC) method is proposed, as it can directly handle multiple control objectives and system constraints within a unified framework. A single cost function is used to regulate motor torque, the auxiliary converter Floating Capacitor (FC) voltage, and the power factor at the main side. This enables coordinated control of both converters without cascaded loops. The predictive controller uses an experimentally identified magnetic model of the SyRel machine, which accounts for magnetic nonlinearities, improving prediction accuracy under dynamic response. Experimental validation confirms the effectiveness of the proposed strategy. The system shows fast dynamic response, robust regulation of control objectives, and consistent unity power factor at the main converter. A comparison with conventional PI-based control highlights the advantages of the proposed method in terms of power factor regulation and dynamic performance, particularly under strong nonlinearities introduced by the FC voltage control. These results confirm that the proposed cost function formulation, with its specific control terms, effectively enables regulation of the overall system.