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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.
Davari et al. (Tue,) studied this question.