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In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC) theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI) method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN) is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system.<\/jats:p>","DOI":"10.1155\/2017\/6034786","type":"journal-article","created":{"date-parts":[[2017,10,17]],"date-time":"2017-10-17T19:31:11Z","timestamp":1508268671000},"page":"1-14","source":"Crossref","is-referenced-by-count":3,"title":["A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0286-6444","authenticated-orcid":true,"given":"Chuanfeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China"},{"name":"School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT9 5AH, 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