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Unlike traditional multi\u2010agent coordination methods that are solved by neural dynamics, the proposed strategy displays greater flexibility, adaptability and scalability. Furthermore, the proposed AMAC strategy is reconstructed as a time\u2010varying complex\u2010valued matrix equation. By introducing a dynamic error function, a fixed\u2010time convergent zeroing neural network (FTCZNN) model is designed for the online solution of the AMAC strategy, with its convergence time upper bound derived theoretically. Finally, the effectiveness and applicability of the coordination control method are demonstrated by numerical simulations and physical experiments. Numerical results indicate that this method can reduce the formation error to the order of  within 1.8\u00a0s.<\/jats:p>","DOI":"10.1049\/cit2.70089","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T14:00:58Z","timestamp":1764079258000},"page":"267-278","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fixed\u2010Time Zeroing Neural Dynamics for Adaptive Coordination of Multi\u2010Agent Systems"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4788-5067","authenticated-orcid":false,"given":"Cheng","family":"Hua","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering Jishou University  Jishou China"}]},{"given":"Xinwei","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Business Jiangnan University  Wuxi 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