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Improved Position Sensorless Resonant Frequency Tracking Control Method for Linear Oscillatory Machine

delete2024-04-01
delete3
PRE
AI
K
Kaiju Liao
W
Wei Xu *
J
Jian Ge
I
Ion Boldea
DOI:10.1109/TIE.2023.3273251delete
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摘要

摘要

En 中文
To improve the reliability of control system and obtain the maximum output efficiency, it is very necessary to carry out the position sensorless piston stroke control and the resonance frequency tracking control for linear oscillatory machine (LOM) at the same time. The conventional resonant frequency tracking control method based on the model reference adaptive system (MRAS) has the problem of low estimation accuracy caused by inaccurate model parameters. In order to obtain more accurate resonant frequency, an improved position sensorless resonant frequency tracking control method is proposed in this article. In this method, the adjustable model is constructed based on the mechanical dynamics equation of LOM to track the system resonant frequency. A hybrid terminal sliding-mode observer (HTSMO) is designed to replace the conventional reference model, which can obtain more accurate velocity reference signal for adjustable model by introducing the current error feedback link, thereby further improving the resonant frequency tracking accuracy. Moreover, a second-order generalized integrator is introduced to filter the estimated velocity signal of HTSMO. The filtered velocity signal is not only used for the adjustable model to track the resonant frequency, but also used to obtain the smooth piston stroke signal for the stroke closed-loop control. Comprehensive simulation and experimental results have proved the effectiveness and the superiority of the proposed method.
Keyword:
Hybrid terminal sliding-mode observer (HTSMO)
linear oscillating machine (LOM)
model reference adaptive system (MRAS)
resonant frequency tracking control

期刊

IEEE Transactions on Industrial Electronics 封面图
IEEE Transactions on Industrial Electronics
IF:
7.2
论文数:
1.8W
被引数:
9.8W

机构

U
Universitatea Politehnica Timisoara
学者数:
2.0K
论文数: 1.4K
被引数: 2
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