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In order to solve the problem, an adaptive sliding mode control based on dynamic recurrent fuzzy neural network is designed to apply in the longitudinal landing system. Firstly, the adaptive sliding mode control method is used to suppress the disturbance of carrier air-wake. Next, the dynamic recurrent fuzzy neural network is applied to compensate the system failure, which further ensures the accurate tracking of ideal glide slop for the carrier-based aircraft. Finally, the effectiveness of the proposed control algorithm is verified by simulation results. Compared with the normal control system of the ACLS, this design can obviously improve the stability of the system and has a good restraint effect on the disturbance of the carrier air-wake.<\/jats:p>","DOI":"10.3233\/jifs-179063","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T13:45:02Z","timestamp":1554212702000},"page":"53-62","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Dynamic recurrent fuzzy neural network-based adaptive sliding control for longitudinal automatic carrier landing system"],"prefix":"10.1177","volume":"37","author":[{"given":"Qidan","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Automation, Harbin Engineering University, Nangang District, Harbin City, Heilongjiang Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibo","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Automation, Harbin Engineering University, Nangang District, Harbin City, Heilongjiang Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.2514\/1.C033175"},{"issue":"11","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/2105682","article-title":"Analysis of global sensitivity of landing variables on landing loads and extreme values of the loads in carrier-based aircrafts","author":"Zhou J.","year":"2018","unstructured":"J.Zhou, J.Zeng and J.Chen, et al.Analysis of global sensitivity of landing variables on landing loads and extreme values of the loads in carrier-based aircrafts, International Journal of Aerospace Engineering (11) (2018), 1\u201314.","journal-title":"International Journal of Aerospace Engineering"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.2514\/3.19978"},{"issue":"18","key":"e_1_3_1_5_2","first-page":"1","article-title":"Adaptive disturbance rejection control for automatic carrier landing system","author":"Wang X.","year":"2016","unstructured":"X.Wang, X.Chen and L.Wen, Adaptive disturbance rejection control for automatic carrier landing system, Mathematical Problems in Engineering (18) (2016), 1\u201312.","journal-title":"Mathematical Problems in Engineering"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2017.06.026"},{"key":"e_1_3_1_7_2","article-title":"TRec: An Efficient Recommendation System for Hunting Passengers with Deep Neural Networks","author":"Huang Zhenhua","year":"2018","unstructured":"ZhenhuaHuang, GuangxuShan, JiujunCheng and JianSun, TRec: An Efficient Recommendation System for Hunting Passengers with Deep Neural Networks, Neural Computing and Applications (2018).","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.2514\/1.54213"},{"key":"e_1_3_1_9_2","volume-title":"AIAA Infotech @ Aerospace 2007 Conference and Exhibit 2006.","author":"Biju Prasad B","unstructured":"Biju, Prasad B and S.Pradeep, Automatic Landing System Design using Feedback Linearization Method.AIAA Infotech @ Aerospace 2007 Conference and Exhibit 2006."},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.2514\/3.21177"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.2514\/3.19978"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.995118"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.868943"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01134"},{"key":"e_1_3_1_15_2","article-title":"Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network \u2013 A novel structure[J]","author":"El-Nagar Ahmad M.","year":"2017","unstructured":"El-Nagar, Ahmad M., Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network \u2013 A novel structure[J]. 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