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Event‐triggered H ∞ filtering for nonlinear discrete‐time stochastic systems with application to vehicle roll stability control
Author(s) -
Zhang Tianliang,
Deng Feiqi,
Shi Peng
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.5248
Subject(s) - nonlinear system , discrete time and continuous time , control theory (sociology) , event (particle physics) , stability (learning theory) , computer science , filtering problem , control (management) , matrix (chemical analysis) , mathematics , kalman filter , artificial intelligence , extended kalman filter , statistics , physics , quantum mechanics , machine learning , materials science , composite material
Summary In this article, the problem of event‐triggered H ∞ filtering for general discrete‐time nonlinear stochastic systems is investigated. An event‐triggered mechanism is introduced to reduce the communication burden by judging whether the measured data should be transmitted. First, two results about the event‐triggered H ∞ filtering for general discrete nonlinear stochastic systems are presented. Second, when the linear discrete‐time stochastic system is considered, the corresponding event‐triggered H ∞ filtering can be designed via linear matrix equality approach. Finally, a numerical example and a practical example about the vehicle roll angle estimation are presented to verify the effectiveness of the proposed new design method.