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For the purpose of eliminating the effect of the disturbance, nonlinear disturbance observer (NDO) technique is used and the disturbance estimation error is guaranteed to be globally exponential convergence. Then, based on the disturbance estimation result and desired trajectory signal, a steady state control input is presented and the optimal tracking problem of original system with external disturbance can be converted into the optimal regulation problem of a nominal error system. Furthermore, a single network-based adaptive dynamic programming (ADP) method is applied to obtain the corresponding optimal feedback control law. Finally, all the signals in closed-loop system are proved to be uniformly ultimately bounded (UUB) and the tracking error can converge to a sufficiently small bound. Simulation tests about NSV attitude system are given to verify the effectiveness of proposed robust optimal flight control scheme.<\/jats:p>","DOI":"10.1177\/0142331219868403","type":"journal-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T07:11:04Z","timestamp":1567062664000},"page":"272-284","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Disturbance observer-based optimal flight control of near space vehicle with external disturbance"],"prefix":"10.1177","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7846-5359","authenticated-orcid":false,"given":"Rongsheng","family":"Xia","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, China"}]},{"given":"Qingxian","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, China"}]},{"given":"Shuyi","family":"Shao","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, China"}]}],"member":"179","published-online":{"date-parts":[[2019,8,29]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2016.2617285"},{"key":"e_1_3_4_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(97)00128-3"},{"key":"e_1_3_4_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0898-1221(97)00276-9"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12555-012-0237-4"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2004.839034"},{"key":"e_1_3_4_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2478397"},{"key":"e_1_3_4_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2010.5531586"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2018.10.005"},{"issue":"6","key":"e_1_3_4_10_1","first-page":"5351","article-title":"Robust predictive control for hypersonic vehicles using recurrent functionl link artifical neural network","volume":"12","author":"Du YL","year":"2010","unstructured":"Du YL, Wu QX, Jiang CS (2010) Robust predictive control for hypersonic vehicles using recurrent functionl link artifical neural network. 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