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onextendedstatebasedkalmanfilterdesignforaclassofnonlineartimevaryinguncertainsystems
Bai Wenyan1; Xue Wenchao2; Huang Yi2; Fang Haitao2
2018
Source Publicationsciencechinainformationscience
ISSN1674-733X
Volume061Issue:004Pages:042201
AbstractThis paper considers the filtering problem for a class of multi-input multi-output systems with nonlinear time-varying uncertain dynamics, random process and measurement noise. An extended state based Kalman filter, with the idea of timely estimating the unknown dynamics, is proposed for better robustness and higher estimation precision. The stability of the proposed filter is rigorously proved for nonlinear timevarying uncertain system with weaker stability condition than the extended Kalman filter, i.e., the initial estimation error, the uncertain dynamics and the noises are only required to be bounded rather than small enough. Moreover, quantitative precision of the proposed filter is theoretically evaluated. The proposed algorithm is proved to be the asymptotic unbiased minimum variance filter for constant uncertainty. The simulation results of some benchmark examples demonstrate the feasibility and effectiveness of the method.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/43702
Collection系统科学研究所
Affiliation1.Beijing Aerospace Automatic Control Institute
2.中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Bai Wenyan,Xue Wenchao,Huang Yi,et al. onextendedstatebasedkalmanfilterdesignforaclassofnonlineartimevaryinguncertainsystems[J]. sciencechinainformationscience,2018,061(004):042201.
APA Bai Wenyan,Xue Wenchao,Huang Yi,&Fang Haitao.(2018).onextendedstatebasedkalmanfilterdesignforaclassofnonlineartimevaryinguncertainsystems.sciencechinainformationscience,061(004),042201.
MLA Bai Wenyan,et al."onextendedstatebasedkalmanfilterdesignforaclassofnonlineartimevaryinguncertainsystems".sciencechinainformationscience 061.004(2018):042201.
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