1. Beijing Aerospace Automatic Control Institute, Beijing 100039, China
2. LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
3. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Corresponding author (email@example.com)
This paper considers the filtering problem for a class of multi-input multi-output systems with nonlinear time-varying uncertain dynamics, random process andmeasurement 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 time-varying 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.
This work was supported by National Natural Science Foundation of China (NSFC) (Grant Nos. 61603380, 61633003-3), and National Basic Research Program of China (Grant No. 2014CB845301). The authors would like to thank professer Lei GUO with Chinese Academy of Sciences for his helpful suggestions.
(Color online) The estimation error of ESKF and EKF (Example 1).
(Color online) The MSE of ESKF and SVF for C1 (Example 2).
(Color online) The estimate error of ESKF and SVF for C2 (Example 2).
(Color online) The MSE of ESKF and SVF for C3 (Example 2).
(Color online) The estimation of ESKF and the state value for C3 (Example 2).
(Color online) The mean square error of ESKF and the diagonal elements of $P_k$ in ESKF for C3protect łinebreak (Example 2).
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