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Reduced‐order observer‐based robust drag‐tracking guidance for uncertain entry vehicles
Author(s) -
Yan Han,
Wang Xinghu,
He Yingzi,
Wei Chunling
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5019
Subject(s) - drag , control theory (sociology) , computer science , observer (physics) , robust control , lift to drag ratio , tracking error , lift (data mining) , tracking (education) , monte carlo method , control engineering , engineering , mathematics , control (management) , control system , aerospace engineering , artificial intelligence , physics , psychology , pedagogy , statistics , electrical engineering , quantum mechanics , data mining
Summary This article studies the drag‐tracking guidance design problem of entry vehicles with low lift‐to‐drag ratio. Taking issues of uncertainty and input saturation into account, we develop a reduced‐order observer‐based robust output feedback guidance law, making the drag‐tracking error converge near zero. Our study not only achieves robust drag‐tracking guidance against inherently existing uncertainty, but also removes the redundant drag estimation in the literature. The Monte Carlo simulation is done to illustrate the advantage of the developed method.