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Firstly, an equivalent linearization data model of each agent is constructed using a pseudo partial derivative approach, where only one parameter needs to be estimated using input\/output data of the controlled MASs. Meanwhile, the DoS attack behavior is described by a Bernoulli distribution process, and both cooperative and competitive relationships among agents are investigated. Moreover, an increment prediction compensator is designed to reduce the effect of DoS attacks. A data-based adaptive predictive bipartite consensus control algorithm is formulated. The corresponding theoretical analysis indicates that tracking errors of MASs with fixed and switching topologies converge to a small range around zero. Finally, several simulations and hardware tests further verify the proposed scheme\u2019s effectiveness.<\/jats:p>","DOI":"10.1177\/09596518241236928","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T08:29:33Z","timestamp":1711528173000},"page":"1231-1241","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks"],"prefix":"10.1177","volume":"238","author":[{"given":"Yeerjiang","family":"Halimu","sequence":"first","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China"},{"name":"School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi, 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