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Low‐complexity ISS state estimation approach with bounded disturbances
Author(s) -
Shen Qiang,
Liu Jieyu,
Zhou Xiaogang,
Zhao Qian,
Wang Qi
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2924
Subject(s) - ellipsoid , bounded function , bounding overwatch , minkowski addition , computational complexity theory , intersection (aeronautics) , noise (video) , stability (learning theory) , mathematics , state (computer science) , computer science , set (abstract data type) , algorithm , control theory (sociology) , mathematical optimization , minkowski space , artificial intelligence , engineering , mathematical analysis , physics , geometry , control (management) , astronomy , machine learning , image (mathematics) , aerospace engineering , programming language
Summary This paper presents a low‐complexity input‐to‐state stable ellipsoidal outer‐bounding state estimation approach with unknown but bounded disturbances. The bounds on the noise are specified by ellipsoids. The feasible set is updated through computing the Minkowski sum and intersection of two ellipsoids. At the observation stage, the observation noise bounding ellipsoid is replaced by a parallelotope containing it. Then, each observation update is transformed into multiple consecutive iterations to intersect ellipsoid with strips, which significantly reduces its per‐update computational complexity. Furthermore, an adaptive selection scheme of the parameters is derived to ensure the stability of the estimation error. As a result, the proposed approach entails stability and delivers a trade‐off between performance and complexity.