Work

Compensating the Measurement Error in Model-Free Predictive Control of Induction Motor via Kalman Filter-Based Ultra-Local Model

Year: 2024

Type: article

Abstract: In model predictive control, ensuring the accuracy and robustness of the prediction model is crucial. A Kalman filter (KF) is a self-correction method commonly used as an observer for state estimation in uncertain applications. Model-free predictive control utilizes an ultra-local model for prediction purposes. Precise measurements and feedback gains are required for accuracy. This study proposes a new ultra-local prediction model based on the KF, replacing the extended state observer (ESO) with the proposed model for disturbance observation. The KF-based prediction model is applied to the model-free predictive control of the induction motor (IM). The method is validated with experimental results, comparing it to the ESO-based prediction model, using a 4 kW IM setup.

Source: IEEE Transactions on Power Electronics

DOI: 10.1109/tpel.2024.3443134

Authors: S. Alireza Davari, Shirin Azadi, Freddy Flores-Bahamonde, Fengxinag Wang, Patrick Wheeler, Jose Rodriguez