Data-driven model predictive control based on discrete space vector modulation for permanent magnet synchronous motor

Improving computational efficiency and reducing the impact of parameter mismatch are important for the model predictive control (MPC) of permanent magnet synchronous motors (PMSM). In this article, an MPC algorithm based on reference voltage vector optimization is proposed. The proposed MPC is based on the discrete space vector modulation (DSVM) to synthesize 38 voltage vectors. This method reduces the number of candidate voltage vectors from 38 to 7 through the reference voltage vector. To overcome the problem of poor parameter robustness, a data prediction model based on behavioral system theory is constructed. This method does not require accurate motor parameters, and only needs input/output data. Then, a reference voltage vector solving method based on the data prediction model is designed. Finally, the experimental results indicate that the method used can reduce computational burden relative to exhaustive DSVM search and exhibit stronger robustness in the case of parameter mismatch.