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Robust unscented Kalman filtering for nonlinear uncertain systems
Author(s) -
Xiong K.,
Wei C. L.,
Liu L. D.
Publication year - 2010
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.190
Subject(s) - unscented transform , kalman filter , control theory (sociology) , extended kalman filter , invariant extended kalman filter , covariance intersection , fast kalman filter , nonlinear system , linearization , computer science , riccati equation , covariance , mathematics , control (management) , artificial intelligence , partial differential equation , statistics , mathematical analysis , physics , quantum mechanics
A derivative‐free robust Kalman filter algorithm is proposed for nonlinear uncertain systems. The unscented transform (UT) is adopted instead of the linearization technique to obtain the solution of the H ∞ filter Riccati equation. A robust unscented Kalman filter (RUKF) is derived to guarantee an optimized upper bound on the estimation error covariance despite the model uncertainties and the approximation error of the UT. The proposed algorithm is applied to a satellite attitude determination system. Simulation results show that the RUKF is more effective than the unscented Kalman filter (UKF) in cases where alignment errors are present. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society