Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs

Abstract

In the velocity control of Permanent Magnet Synchronous Motors (PMSMs), Deadbeat Predictive Current Controllers (DPCCs) are renowned for their excellent dynamic performance and constant switching frequency. However, achieving precise velocity regulation remains challenging due to the nonlinearities introduced by two-level voltage source inverter (2L-VSI). Specifically, the dead time inherent in 2L-VSI results in voltage distortion, which generates parasitic harmonics in the system. These harmonics degrade control accuracy, cause a current ripple, and can lead to performance degradation or even system instability, compromising reliable operation. This article proposes an innovative solution: Artificial Neural Network-Based Deadbeat Predictive Current Control (ANN-DPCC) integrated with dead-time compensation to address these issues. This approach effectively suppresses the current ripple and significantly reduces total harmonic distortion (THD). Simulation results validate that ANN-DPCC with dead-time compensation outperforms traditional DPCC by improving response times, enhancing steady-state accuracy, and minimizing current distortions. This novel strategy significantly advances PMSM control, offering precise velocity regulation, improved reliability, and superior system performance for demanding applications