An Improved Model Predictive Current Control of Permanent Magnet Machine with Integrated MRAS-based Online Parameter Identification

Finite control set model predictive control (FCS-MPC) is a promising method for permanent magnet machine control for its merits in dynamic performance and multi-objective control. However, the parameter mismatch issue and computation burden have restricted its application. An improved model predictive current control of permanent magnet machine with integrated MRAS-based online parameter identification is proposed in this paper, which can deal with the parameter mismatch issue without significantly increasing the computation burden. Different from existing methods, an integrated design is adopted to combine the FCS-MPC and the model reference adaptive system (MRAS) together, which can reuse some prediction results in both the FCS-MPC and the reference model of the MRAS. Experimental results validate the effectiveness of the proposed method in parameter identification and computation burden releasing.