Data-Driven Model Predictive Current Control for Synchronous Machines: a Koopman Operator Approach

In this paper, a data-driven continuous control set model predictive current control (CCS-MPCC) scheme for permanent magnet synchronous motors (PMSMs) is proposed. The model of the motor used in the model predictive control (MPC) strategy is obtained from collected measurements using the Koopman operator (KO) theory. Experimental results on a 500W PMSM show that the obtained model has yielded excellent prediction accuracy, and that it is capable of being incorporated within a real-time CCS-MPCC scheme in the sub-millisecond typically available sampling time for the current control loop of synchronous motors.