An Improved Deadbeat Predictive Current Control Based on Parameter Identification for PMSM
Lanbing Wang, Shuo Zhang, Chengning Zhang, Ying Zhou
Abstract
Based on multi-parameter identification, this paper proposes an improved deadbeat predictive current control (DPCC) scheme. This scheme effectively solves the problem of underdetermined equations in multi-parameter identification by establishing two current prediction error models that include uncertain components of motor parameters. Additionally, the method facilitates the decoupling of d/q-axis inductance, stator resistance, and rotor flux linkage. The proposed method employs a discrete model reference adaptive system (MRAS) that ensures fast convergence and simple application, enabling accurate identification of motor parameters. It is noteworthy that manual compensation of the dead time voltage is required before the identification. According to simulation and experiments, this method offers several advantages, such as a short convergence time, a low recognition error, and no need to manually modify algorithm parameters. As a result, the proposed method effectively solves issues of unsatisfactory current and torque output caused by motor parameter mismatch.