Data-Driven Fully Distributed Fault-Tolerant Consensus Control for Nonlinear Multi-Agent Systems: An Observer-Based Approach

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Bibliografiset tiedot
Julkaisussa:Mathematics vol. 13, no. 22 (2025), p. 3582-3598
Päätekijä: Zhao Yuyang
Muut tekijät: Li Dongnan, Li, Yunlong, Gong Dawei, Chen Jiaoyuan, Song, Shijie, Zhu Minglei
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MDPI AG
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100 1 |a Zhao Yuyang  |u School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 
245 1 |a Data-Driven Fully Distributed Fault-Tolerant Consensus Control for Nonlinear Multi-Agent Systems: An Observer-Based Approach 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a This paper introduces a novel observer-based, fully distributed fault-tolerant consensus control algorithm for model-free adaptive control, specifically designed to tackle the consensus problem in nonlinear multi-agent systems. The method addresses the issue of followers lacking direct access to the leader’s state by employing a distributed observer that estimates the leader’s state using only local information from the agents. This transforms the consensus control challenge into multiple independent tracking tasks, where each agent can independently follow the leader’s trajectory. Additionally, an extended state observer based on a data-driven model is utilized to estimate unknown actuator faults, with a particular focus on brake faults. Integrated into the model-free adaptive control framework, this observer enables real-time fault detection and compensation. The proposed algorithm is supported by rigorous theoretical analysis, which ensures the boundedness of both the observer and tracking errors. Simulation results further validate the algorithm’s effectiveness, demonstrating its robustness and practical viability in real-time fault-tolerant control applications. 
653 |a Control theory 
653 |a Control algorithms 
653 |a Communication 
653 |a Estimates 
653 |a Fault tolerance 
653 |a Controllers 
653 |a Distributed control systems 
653 |a Design 
653 |a Adaptive control 
653 |a Explicit knowledge 
653 |a Multiagent systems 
653 |a Systems stability 
653 |a Nonlinear systems 
653 |a Nonlinear control 
653 |a Real time 
653 |a Fault detection 
653 |a Tracking errors 
653 |a Actuators 
653 |a State observers 
653 |a Adaptive algorithms 
700 1 |a Li Dongnan  |u Heilongjiang Provincial Climate Center, Harbin 150030, China 
700 1 |a Li, Yunlong  |u The Third Military Representative Office of the Air Force Armaments Department in Harbin, Harbin 150010, China 
700 1 |a Gong Dawei  |u School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 
700 1 |a Chen Jiaoyuan  |u School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 
700 1 |a Song, Shijie  |u Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 614202, China 
700 1 |a Zhu Minglei  |u Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 614202, China 
773 0 |t Mathematics  |g vol. 13, no. 22 (2025), p. 3582-3598 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3275541794/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3275541794/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3275541794/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch