Compensating the Measurement Error in Model-free Predictive Control of Induction Motor via Kalman Filter-based Ultra-local Model

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

Abstract

In model predictive control, ensuring the accuracy and robustness of the prediction model is crucial. A Kalman filter 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 Kalman filter, replacing the extended state observer with the proposed model for disturbance observation. The Kalman filter-based prediction model is applied to the model-free predictive control of the induction motor. The method is validated with experimental results, comparing it to the extended state observer-based prediction model, using a 4kW induction motor setup.

Citation

Davari, S. A., Azadi, S., Flores-Bahamonde, F., Wang, F., Wheeler, P., & Rodriguez, J. (2024). Compensating the Measurement Error in Model-free Predictive Control of Induction Motor via Kalman Filter-based Ultra-local Model. IEEE Transactions on Power Electronics, https://doi.org/10.1109/TPEL.2024.3443134

• Journal Article Type: Article
• Acceptance Date: Aug 13, 2024
• Online Publication Date: Aug 13, 2024
• Publication Date: Aug 13, 2024
• Deposit Date: Sep 16, 2024
• Journal: IEEE Transactions on Power Electronics
• Print ISSN: 0885-8993
• Electronic ISSN: 1941-0107
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TPEL.2024.3443134
• Keywords: Predictive models , Observers , Kalman filters , Stators , Predictive control , Mathematical models , Measurement uncertainty
• Public URL: https://nottingham-repository.worktribe.com/output/38649346
• Publisher URL: https://ieeexplore.ieee.org/document/10636071