Open AccessRobust state estimation for uncertain linear discrete systems with d‐step state delay
Author(s) -
Wang Jing,
Mao Yao,
Li Ziqiang,
Gao Junwei,
Liu Huabo
Publication year - 2021
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12153
Subject(s) - estimator , control theory (sociology) , state (computer science) , kalman filter , mathematics , covariance , state estimator , bounded function , state space , filter (signal processing) , state vector , linear system , computer science , mathematical optimization , algorithm , statistics , control (management) , artificial intelligence , mathematical analysis , physics , classical mechanics , computer vision
This paper discusses the state estimation problems of an uncertain linear discrete time‐varying state space model with d‐step state delay. Based on the principle of minimising the expectation of estimation errors and the method of state augmentation, a robust state estimation algorithm is proposed. Specially, this estimator retains the form of Kalman‐like filter and the characteristics of fast recursive calculation. Moreover, the conditions of bounded estimation error covariance and the proof of asymptotic unbiasedness of the filter are given. Finally, numerical examples are used to verify the effectiveness and the wide applicability of this estimator.