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Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer.

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Title: Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer.
Authors: Feng, Fu1,2 (AUTHOR), Yang, Jie1,2 (AUTHOR), Hu, Hailin1,3 (AUTHOR) huhailin06@163.com, Liang, Jianbin1,2 (AUTHOR), Yu, Shiyan1,2 (AUTHOR)
Source: Transactions of the Institute of Measurement & Control. May2026, Vol. 48 Issue 8, p1433-1444. 12p.
Subjects: Linear induction motors, Kalman filtering, Sensorless control systems, Nonlinear difference equations, Predictive control systems, Adaptive estimation (Statistics), Thrust
Abstract: This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance. [ABSTRACT FROM AUTHOR]
ISSN:01423312
DOI:10.1177/01423312241307713