Compensating the Measurement Error in Model-Free Predictive Control of Induction Motor via Kalman Filter-Based Ultra-Local Model
S. Alireza Davari, Shirin Azadi, Freddy Flores‐Bahamonde, Fengxinag Wang, Patrick Wheeler, José Rodríguez
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.