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Event‐triggered UKF for nonlinear dynamic systems with packet dropout
Author(s) -
Li Li,
Yu Dongdong,
Xia Yuanqing,
Yang Hongjiu
Publication year - 2017
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.3790
Subject(s) - dropout (neural networks) , control theory (sociology) , network packet , kalman filter , computer science , estimator , nonlinear system , filter (signal processing) , covariance , extended kalman filter , event (particle physics) , filtering problem , mathematics , computer network , artificial intelligence , statistics , physics , control (management) , quantum mechanics , machine learning , computer vision
Summary In this paper, the event‐triggered nonlinear filtering problem is investigated for nonlinear dynamic systems over a wireless sensor network with packet dropout. Measurements are transmitted to a remote estimator only when a specific event happens for a reduction of communication cost. An event‐triggered unscented Kalman filter related to trigger threshold is derived. It is shown that the prediction error covariance of the proposed filter is bounded and converges to a steady value if the threshold and packet dropout rate are small enough. Sufficient conditions are obtained to ensure stochastic stability of the filter, where a critical value of the threshold exists. Two examples are given to illustrate the effectiveness of the proposed filter. Copyright © 2017 John Wiley & Sons, Ltd.