@inproceedings{f8253a3b2270469987f1af26eec3a9a6,
title = "Extended Kalman Filter as the Prediction Model in Sensorless Predictive Control of Induction Motor",
abstract = "The accuracy and robustness of the prediction model are always critical issues in model predictive control (MPC). This is more serious in sensorless applications because there are more uncertain parameters in the control system. Extended Kalman filter (EKF) is known as one of the self-correction methods. It has been widely used in sensorless applications as the observer with the aim of speed estimation. In this research, a new prediction model based on EKF is proposed and studied. This study aims to investigate the effectiveness of the EKF-based prediction model in the presence of parameter mismatch in the sensorless application of the predictive method. The simulation results verify the validity of the proposed method.",
keywords = "model predictive control, ro-bustness, sensorless drives",
author = "Davari, {S. Alireza} and Shirin Azadi and Luca Tarisciotti and Cristian Garcia and Zhenbin Zhang and Fengxinag Wang and Jose Rodriguez",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023 ; Conference date: 16-06-2023 Through 19-06-2023",
year = "2023",
doi = "10.1109/PRECEDE57319.2023.10174332",
language = "English",
series = "2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023",
}