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

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.