Model-Free Predictive Current Control of PMSM Drives Based on Extended State Observer Using Ultralocal Model

Abstract

Conventional model predictive current control (MPCC) is a powerful control strategy for three-phase inverters that has the advantages of simple concept, quick response, easy implementation, and good performance. However, MPCC is sensitive to machine parameter variation, and the performance degrades substantially if a mismatch exists between the model parameters and real machine parameters. Model-free predictive current control (MFPCC) based on an ultralocal model, which uses only the input and output of the system without considering any motor parameters, has been proposed to solve this problem in this article. Since parameters are not required, the robustness of the control system is improved. However, conventional MFPCC based on an ultralocal model uses many control parameters, which increases the tuning work. Furthermore, the control performance is not ideal at low sampling frequency. This article proposes an improved MFPCC based on the extended state observer of PMSM drives that does not require motor parameters and needs less tuning work and lower computational time while achieving the better performance in terms of current harmonics, tracking error, and dynamic overshoot. The proposed method is compared to conventional MPCC and MFPCC, and the effectiveness is confirmed by the simulation and experimental results.