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Force Ripple Estimation and Compensation of PMLSM With Incremental Extended State Modeling-Based Kalman Filter: A Practical Tuning Method

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This paper focuses on the force ripple estimation and compensation with the Kalman filter for the permanent magnet linear synchronous machine (PMLSM). The force ripple dynamics is firstly modeled as… Click to show full abstract

This paper focuses on the force ripple estimation and compensation with the Kalman filter for the permanent magnet linear synchronous machine (PMLSM). The force ripple dynamics is firstly modeled as a higher-order integrator subsystem with fully considering its inherent nonlinear and time-varying characteristic. The motion system dynamics is then extended with modeling the force ripple as an extra state. The main idea for the accurate force ripple estimation is to construct an incremental extended state modeling-based Kalman filter (IESM-KF) for reducing the calculation cost as the higher-order dynamics of the force ripple is considered. And also, a simple and practical parameter tuning method for the IESM-KF is proposed with injecting a square-wave current disturbance to the position controller’s output under the cascade position-current closed loop. The inevitable time delay of the mechanical system is estimated with the sine-sweep-based model identification and is further considered in the IESM-KF design. Detailed experimental results are given to validate the effectiveness of the force ripple compensation with the IESM-KF and the corresponding parameter tuning method.

Keywords: ripple estimation; force ripple; force; tuning method; kalman filter

Journal Title: IEEE Access
Year Published: 2019

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