Data-Driven Continuous-Set Predictive Current Control for Synchronous Motor Drives

Abstract

Optimization-based control strategies are an affirmed research topic in the area of electric motor drives. These methods typically rely on the accurate parametric representation of equations of a motor. In this article, we present the transition from model-based to data-driven optimal control strategies. We start from the model-predictive control paradigm, which uses the voltage balance model of the motor. Then, we discuss the prediction error method, where a state-space model is identified from data, without parameterization. Moving toward data-driven controls, we present the subspace predictive control, where a reduced model is constructed based on the singular value decomposition of raw data. The final step is represented by a complete data-driven approach, named data-enabled predictive control, in which raw data are not encoded into a model but directly used in the controller. The theory behind these techniques is reviewed and applied for the first time to the design of the current controller of synchronous permanent magnet motor drives. Design guidelines are provided to practitioners for the proposed application, and a way to address offset-free tracking is discussed. Experimental results demonstrate the feasibility of the real-time implementation and provide comparisons between the model-based and data-driven controls.