{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T20:58:50Z","timestamp":1778619530136,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T00:00:00Z","timestamp":1539561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Transactions of the Institute of Measurement and Control"],"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:p>This paper presents a distributed adaptive formation control method for uncertain multiple quadrotor systems under a directed graph that characterizes the interaction among the leader and followers. The proposed approach is based on an adaptive dynamic surface control, consensus algorithm and graph theory, where the system uncertainties are approximately modelled by interval type-2 fuzzy neural networks. The adaptive laws of interval type-2 fuzzy neural network parameters are derived from the stability analysis. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and the leader-follower formation goal can be asymptotically achieved. The developed control scheme is applied to the followers of quadrotor systems for performance evaluations. Simulation results are also provided to compare with the existing methods and reveal the superiority of the proposed adaptive formation controller.<\/jats:p>","DOI":"10.1177\/0142331218788115","type":"journal-article","created":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T07:32:52Z","timestamp":1539588772000},"page":"1861-1879","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Distributed adaptive dynamic surface formation control for uncertain multiple quadrotor systems with interval type-2 fuzzy neural networks"],"prefix":"10.1177","volume":"41","author":[{"given":"Teh-Lu","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Engineering Science, National Cheng-Kung University, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Shou","family":"Chan","sequence":"additional","affiliation":[{"name":"National Chung-Shan Institute of Science and Technology, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Juh","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication, Shu-Te University, Taiwan, 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