From Model Predictive Control to Data Driven Control to Meet High Efficiency Objective of a PMSM Drive System

In this paper is proposed a Data Driven Control (DDC) law of a Permanent Magnet Synchronous Machine (PMSM) drive as an alternative to the Model Predictive Control (MPC) strategy. The DDC method is designed for the inner control structure of the PMSM drive, the outer speed loop being employed by a classical linear PI controller. Thus, the DDC is based on build up a database of the relevant quantities as electromagnetic torque, speed, voltages and current, all obtained in steady - state regime with MPC strategy, being selected for the highest efficiency values for a given pair of speed and torque. The learning process of the DDC strategy by a mutidimesional interpolation method leads to obtain high performances in both steady - state and dynamic regimes, without any additional optimal current reference command. A comparative analysis done in Matlab simulation environment of the results obtained by both MPC and DDC control laws shows the effectiveness of the last strategies. The DDC method provide a robust optimal control under energetic constraints, being more suitable then the classical MPC law that can not offers reliable results for a large area of conditions in practice.