Quantification of Valve Stiction in Control Loops Using Adaptive Differential Evolution Algorithm

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies.

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Publication Details

Published
2024-11-01
DOI
https://doi.org/10.1109/cac63892.2024.10864973
Citations
1
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.33

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article

Quantification of Valve Stiction in Control Loops Using Adaptive Differential Evolution Algorithm

Yang Dong, Wanke Yu, Xun Lang, Lei Xie et al.
1 citations
Iterative Learning Control Systems
0.33
article

Quantification of Valve Stiction in Control Loops Using Adaptive Differential Evolution Algorithm

Yang Dong, Wanke Yu, Xun Lang, Lei Xie, Songhua Liu, Jiande Wu
article en
1 citations

Abstract

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies.

Yunnan University (CN), China University of Geosciences (CN), State Key Laboratory of Industrial Control Technology (CN), Zhejiang University of Technology (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 36%
Iterative Learning Control Systems
0.33
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Quantification of Valve Stiction in Control Loops Using Adaptive Differential Evolution Algorithm — Yang Dong, Wanke Yu, et al. · (2024) | TGRS Research Map | TGRS