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An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort\u2019s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor.<\/jats:p>","DOI":"10.1155\/2019\/6460156","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T23:31:20Z","timestamp":1546558280000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6088-9954","authenticated-orcid":false,"given":"Jes\u00fas Enrique","family":"Sierra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1993-8368","authenticated-orcid":false,"given":"Matilde","family":"Santos","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6879419"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1515\/kbo-2015-0126"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"GrassiR. 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