期刊
AUTOMATICA
卷 99, 期 -, 页码 203-212出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2018.10.029
关键词
Two-dimensional systems; Kalman filter; Uncertain systems; Time-varying systems; Least squares method
资金
- National Natural Science Foundation of China [61773242]
- National Science Fund for Excellent Young Scholars of China [61822308]
- Shandong Provincial Key R&D Program, China [2018GGX104025]
- Shandong Provincial Natural Science Foundation, China [ZR2017QF007]
The robust Kalman filter design problem for two-dimensional uncertain linear discrete time-varying systems with stochastic noises is investigated in this study. First, we prove that the solution to a certain deterministic regularized least squares problem constrained by the nominal two-dimensional system model is equivalent to the generalized two-dimensional Kalman filter. Then, based on this relationship, the robust state estimation problem for two-dimensional uncertain systems with stochastic noises is interpreted as a deterministic robust regularized least squares problem subject to two-dimensional dynamic constraint. Finally, by solving the robust regularized least squares problem and using a simple approximation, a recursive robust two-dimensional Kalman filter is determined. A heat transfer process serves as an example to show the properties and efficacy of the proposed filter. (C) 2018 Elsevier Ltd. All rights reserved.
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