Active Compression on Unknown Disturbance and Uncertainty via Extended State Observer

Jiuqiang Deng; Luyao Zhang; Wenchao Xue; Qiliang Bao; Yao Mao

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

J. Deng, L. Zhang, W. Xue, Q. Bao, and Y. Mao, “Active compression on unknown disturbance and uncertainty via extended state observer,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1–15, Sept. 2025.

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J. Deng, L. Zhang, W. Xue, Q. Bao, and Y. Mao, “Active compression on unknown disturbance and uncertainty via extended state observer,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1–15, Sept. 2025.

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Active Compression on Unknown Disturbance and Uncertainty via Extended State Observer

Funds: This work was supported by the Special Research Assistant Program, Chinese Academy of Sciences, China (E329691C21), Sichuan Science and Technology Program, China (2024NSFSC1481), and National Natural Science Foundation of China (92471204, 62271109)

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Author Bio:
Jiuqiang Deng received the B.S. degree in automation from Southwest Jiaotong University, and the Ph.D. degree in signal and information processing from the University of Chinese Academy of Sciences in 2017, and 2022, respectively. He is currently a postdoctoral researcher with the Institute of Optics and Electronics, Chinese Academy of Sciences. His research interests include disturbance estimation and rejection control

Luyao Zhang received the B.S. degree in electrical engineering and automation from Northeastern University at Qinhuangdao in 2019, and the M.S. degree in electric machines and electric apparatus from Yangzhou University in 2023. He is currently a joint Ph.D. student majoring in instrument science and technology at the Institute of Optics and Electronics, Chinese Academy of Sciences and University of Electronic Science and Technology of China. His research interests include active disturbance rejection control and distributed estimation

Wenchao Xue (Member, IEEE) received the B.S. degree in applied mathematics from Nankai University in 2007, and the Ph.D. degree in control theory from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2012. He is currently a Professor with the Key Laboratory of System and Control, Chinese Academy of Sciences. His research interests include nonlinear uncertain systems control, nonlinear uncertain systems filter, and distributed filter. Dr. Xue is an Associate Editor for the IFAC Journal of Control Engineering Practice

Qiliang Bao received the B.S. degree in control engineering from Northwestern Polytechnical University, Xi’an, China, in 1989, and the M.A. degree in optoelectronic engineering from the University of Electronic Science and Technology of China in 2004. He is currently a Professor at the Institute of Optics and Electronics, Chinese Academy of Sciences. His research interests include deep learning and motion control

Yao Mao (Member, IEEE) received the B.S. degree in automation from Chongqing University, and the Ph.D. degree in signal and information processing from the Institute of Optics and Electronics, Chinese Academy of Sciences in 2001, 2012, respectively. Since 2001, he has worked on photoelectric tracking control with the Institute of Optics and Electronics, Chinese Academy of Sciences, where he is currently a Professor. His research interests include machine learning, predictive filtering, and control theory
Corresponding author: Wenchao Xue, e-mail: wenchaoxue@amss.ac.cn; Yao Mao, e-mail: maoyao@ioe.ac.cn
Received Date: 2024-12-24
Accepted Date: 2025-02-17

Available Online: 2025-08-05

Abstract

Abstract

Extended state observer (ESO) is heavily limited by the unknown disturbance and its derivative, which requires high observing gains to decrease estimating error, resulting in serious noise sensitivity. To modify the disturbance estimation characteristics encountered by the observer, the active compression extended state observer (ACESO) is proposed in this study. The ACESO decreases the bound of residual lumped disturbance and its derivative by actively compressing the initial lumped disturbance, without relying on prior knowledge. The stability constraint and convergence results of ACESO are analyzed and compared with ESO theoretically. The results show that the ACESO mitigates the trade-off between noise sensitivity and high-gain observation. Benefiting from active compression, the ACESO has substantially less noise sensitivity than the ESO, while obtaining the same and even better estimating performance than the ESO. In addition, the nonlinear ACESO is discussed, which automatically balances the contradiction between estimation and convergence. Simulations and experiments demonstrate the effectiveness of the proposed methods.
- Active compression,
- extended state observer (ESO),
- high-gain observation,
- noise sensitivity

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References(47)

References

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Active Compression on Unknown Disturbance and Uncertainty via Extended State Observer

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