PMSM Deadbeat Predictive Current Control Based on Extreme Learning Machine

Abstract

In order to strengthen the tracking performance and robustness of permanent magnet synchronous motor (PMSM) system, a deadbeat predictive current control (DPCC) based on extreme learning machine (ELM) is come up. Since PMSM is susceptible to uncertainties such as external disturbances and parameter changes, the uncertainty factors are introduced in the mathematical model. The uncertainty of the system is approximated by the ELM, then the speed tracking of the permanent magnet synchronous motor is realized, and the stability is certificated by establishing the Lyapunov function. In addition, DPCC method of PMSM is proposed, which is equivalent to high-gain proportional control and improves the performance of the PMSM. Finally, the simulation experiments are carried out in nominal case and parameter mismatch case respectively, through the comparative study of system simulation, the results indicate that contrast with the traditional control method, The ELM-DPCC proposed in this paper has better speed tracking performance and robustness.