Finite-time synchronization control for multi-motor driving systems with input saturation via privacy-preserving observers

Abstract This article explores the privacy-preserving synchronization control problem of multi-motor driving systems. A privacy-preserving observer (PPO) using state decomposition is introduced to ensure motors’ state synchronization and information security during network transmission. To enhance convergence speed and estimate unknown disturbances, a finite-time extended state observer (FTESO) is proposed. Then, an adaptive control protocol based on a back-stepping strategy and barrier Lyapunov function is developed to ensure that the synchronization error converges to zero within a finite time. Finally, the effectiveness of the proposed method is demonstrated through a numerical example.

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Publication Details

Journal
Physica Scripta
Published
2025-03-27
DOI
https://doi.org/10.1088/1402-4896/adc63d
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Finite-time synchronization control for multi-motor driving systems with input saturation via privacy-preserving observers

Liang Shan, Xin Huang, Chenglin Liu
Physica Scripta
Iterative Learning Control Systems
article

Finite-time synchronization control for multi-motor driving systems with input saturation via privacy-preserving observers

Liang Shan, Xin Huang, Chenglin Liu
article en

Abstract

Abstract This article explores the privacy-preserving synchronization control problem of multi-motor driving systems. A privacy-preserving observer (PPO) using state decomposition is introduced to ensure motors’ state synchronization and information security during network transmission. To enhance convergence speed and estimate unknown disturbances, a finite-time extended state observer (FTESO) is proposed. Then, an adaptive control protocol based on a back-stepping strategy and barrier Lyapunov function is developed to ensure that the synchronization error converges to zero within a finite time. Finally, the effectiveness of the proposed method is demonstrated through a numerical example.

Physica ScriptaVol. 100(5)
Jiangnan University (CN), Nanjing University of Science and Technology (CN)
Openalex Percentile: Top 94%
Iterative Learning Control Systems
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