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The state predictor is used to predict the state of neighbor agents during periodic DoS attacks and maintain consistent control of multiple unmanned systems under DoS attacks. Considering the existing prediction error between the actual state and the predicted state, the estimated error is regarded as the uncertainty system disturbance, which is dealt with by the designed disturbance observer. The estimated result is used in the design of the consistent controller to compensate for the system uncertainty error term. Furthermore, this paper investigates dynamic event-triggered consensus controllers to improve resilience and consensus under periodic DoS attacks and reduce the frequency of actuator output changes. It is proved that the Zeno behavior can be excluded. Finally, the resilience and consensus capability of the proposed controller and the superiority of introducing a state predictor are demonstrated through numerical simulations.<\/jats:p>","DOI":"10.1051\/sands\/2023017","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T18:58:54Z","timestamp":1687978734000},"page":"2023017","source":"Crossref","is-referenced-by-count":0,"title":["Event-triggered resilient consensus control of multiple unmanned systems against periodic DoS attacks based on state predictor"],"prefix":"10.1051","volume":"2","author":[{"given":"Haichuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziquan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9731-5943","authenticated-orcid":false,"given":"Youmin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"250","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"R1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.arcontrol.2008.03.008","volume":"32","author":"Zhang","year":"2008","journal-title":"Ann Rev Control"},{"key":"R2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cja.2021.04.022","volume":"35","author":"Yu","year":"2022","journal-title":"Chin J Aeronaut"},{"key":"R3","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/00207179.2017.1352104","volume":"92","author":"Shen","year":"2019","journal-title":"Int J Control"},{"key":"R4","first-page":"2022004","volume":"1","author":"Ding","year":"2022","journal-title":"Secur Saf"},{"key":"R5","first-page":"2022001","volume":"1","author":"Wu","year":"2022","journal-title":"Secur Saf"},{"key":"R6","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/TNNLS.2019.2915376","volume":"31","author":"Shen","year":"2020","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"R7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s40435-018-0495-y","volume":"8","author":"Fattahi","year":"2020","journal-title":"Int J Dyn Control"},{"key":"R8","first-page":"2022005","volume":"1","author":"Gao","year":"2022","journal-title":"Secur Saf"},{"key":"R9","doi-asserted-by":"crossref","first-page":"3595","DOI":"10.1109\/TII.2021.3126644","volume":"18","author":"He","year":"2022","journal-title":"IEEE Trans Ind Inf"},{"key":"R10","doi-asserted-by":"crossref","first-page":"100969","DOI":"10.1016\/j.nahs.2020.100969","volume":"39","author":"Liu","year":"2021","journal-title":"Nonlinear Anal Hybrid Syst"},{"key":"R11","doi-asserted-by":"crossref","first-page":"3714","DOI":"10.1109\/TAC.2019.2953210","volume":"65","author":"Zhu","year":"2020","journal-title":"IEEE Trans Autom Control"},{"key":"R12","doi-asserted-by":"crossref","unstructured":"Feng Z and Hu GQ. 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