Distributed Control Algorithm for Multi‐Agent Cooperation: Leveraging Spatial Information Perception
In the natural world, coordinated behaviors like group migration and collective defense exemplify the capacity of biological populations to process spatial information and adapt their actions correspondingly. Drawing inspiration from these natural phenomena, researchers have introduced diverse multi...
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| Published in: | International journal of robust and nonlinear control |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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11.08.2025
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| ISSN: | 1049-8923, 1099-1239 |
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| Abstract | In the natural world, coordinated behaviors like group migration and collective defense exemplify the capacity of biological populations to process spatial information and adapt their actions correspondingly. Drawing inspiration from these natural phenomena, researchers have introduced diverse multi‐agent cooperative control strategies, such as fixed neighborhood algorithms, fixed number algorithms, and farthest neighborhood algorithms. Nevertheless, these approaches encounter obstacles including sluggish convergence rates, limited resilience to interference, and constrained interpretability. To overcome these limitations, this paper introduces an innovative distributed multi‐agent cooperative control algorithm leveraging an attention mechanism. The algorithm dynamically modulates the information perception weights of agents, facilitating effective coordination in intricate environments. Furthermore, it integrates a pioneering obstacle avoidance strategy, guaranteeing resilient performance in dynamic and densely populated environments. In comparison to existing approaches, the proposed algorithm demonstrates accelerated convergence, enhanced interference resistance, and improved scalability in formation control, all while preserving theoretical interpretability. This study offers a fresh perspective and innovative methodology for advancing cooperative control in multi‐agent systems, with wide‐ranging applications in fields like robotics and autonomous systems. |
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| AbstractList | In the natural world, coordinated behaviors like group migration and collective defense exemplify the capacity of biological populations to process spatial information and adapt their actions correspondingly. Drawing inspiration from these natural phenomena, researchers have introduced diverse multi‐agent cooperative control strategies, such as fixed neighborhood algorithms, fixed number algorithms, and farthest neighborhood algorithms. Nevertheless, these approaches encounter obstacles including sluggish convergence rates, limited resilience to interference, and constrained interpretability. To overcome these limitations, this paper introduces an innovative distributed multi‐agent cooperative control algorithm leveraging an attention mechanism. The algorithm dynamically modulates the information perception weights of agents, facilitating effective coordination in intricate environments. Furthermore, it integrates a pioneering obstacle avoidance strategy, guaranteeing resilient performance in dynamic and densely populated environments. In comparison to existing approaches, the proposed algorithm demonstrates accelerated convergence, enhanced interference resistance, and improved scalability in formation control, all while preserving theoretical interpretability. This study offers a fresh perspective and innovative methodology for advancing cooperative control in multi‐agent systems, with wide‐ranging applications in fields like robotics and autonomous systems. |
| Author | He, Ming Han, Wei Liu, Chengzhuo Liu, Sicong Chen, Haotian |
| Author_xml | – sequence: 1 givenname: Sicong surname: Liu fullname: Liu, Sicong organization: School of Command and Control Engineering Army Engineering University of PLA Nanjing China, School of Internet of Things and Intelligent Engineering Jiangsu Vocational Institute of Commerce Nanjing China – sequence: 2 givenname: Ming orcidid: 0009-0005-6705-9108 surname: He fullname: He, Ming organization: School of Command and Control Engineering Army Engineering University of PLA Nanjing China – sequence: 3 givenname: Wei surname: Han fullname: Han, Wei organization: School of Command and Control Engineering Army Engineering University of PLA Nanjing China – sequence: 4 givenname: Haotian surname: Chen fullname: Chen, Haotian organization: School of Command and Control Engineering Army Engineering University of PLA Nanjing China – sequence: 5 givenname: Chengzhuo surname: Liu fullname: Liu, Chengzhuo organization: School of Command and Control Engineering Army Engineering University of PLA Nanjing China |
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