An Improved Model-free Predictive Current Control for Synchronous Reluctance Motor Drives

Model-free predictive current control(MFPCC) has been widely studied in the field of motor drive because of its intrinsic robustness to internal and external disturbances of the system, and can achieve high performance control of synchronous reluctance motor(SynRM). The traditional MFPCC method stores the current difference corresponding to the voltage vector at each time in the lookup table(LUT), and then predicts the optimal voltage vector at the next time, but there are problems of current difference update stagnation and unsatisfactory steady-state performance. To solve these problems, an improved MFPCC method is proposed. The motor model is expressed as an ultra-local model, and the LUT is updated timely by estimating the gain term and disturbance term online, which solves the current differential update stagnation problem. In addition, a more accurate voltage vector is obtained by using the extended finite state set to improve the steady-state performance. Finally, the proposed method is compared with the traditional MFPCC method, and the simulation and experimental results show that the proposed method effectively solves the problem of current update stagnation, and has good dynamic and static performance in the full speed range.