Gaussian Noise Suppression in Deadbeat Predictive Current Control of Permanent Magnet Synchronous Motors Based on Augmented Fading Kalman Filter

The ultra-local model-based deadbeat predictive current control (ULM-DPCC) is attractive to a permanent magnet synchronous motors (PMSM) drive for its rapid current response and robustness to the parameter changes. However, the accuracy suffers from the model noise and measurement noise in the application of ULM-DPCC to a PMSM drive. To improve the precision of current control under the uncertain Gaussian noise, an augmented fading Kalman filter-based ULM-DPCC (ULM-DPCC-AFKF) for the PMSM drive is proposed. First, the Kalman filter (KF) is employed to estimate the unknown part in ULM-DPCC. Second, to ameliorate the estimation performance under uncertain noise conditions, an augmented fading Kalman filter (AFKF) is proposed to adapt to changes in noise, and an appropriate reference voltage vector is obtained by combining ULM-DPCC and AFKF. In AFKF, the augmented fading factors are derived to make the AFKF observer work optimally, and high-precision current control for PMSM is achieved. Last, the availability of the proposed ULM-DPCC-AFKF strategy is proved via experiment and simulation under the different Gaussian noise.