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Optimal‐switched extended H ∞ filter for nonlinear systems with stochastic uncertainties
Author(s) -
Li Yuankai,
Ding Liang,
Jing Zhongliang
Publication year - 2020
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.4915
Subject(s) - robustness (evolution) , control theory (sociology) , mathematics , nonlinear system , filter (signal processing) , mathematical optimization , redundancy (engineering) , computer science , biochemistry , chemistry , physics , control (management) , quantum mechanics , artificial intelligence , computer vision , gene , operating system
Summary The extended H ∞ filter (EH ∞ F) is a conservative solution with infinite‐horizon robustness for the state estimation problem regarding nonlinear systems with stochastic uncertainties, which leads to excessive costs in terms of filtering optimality and reduces the estimation precision, particularly when uncertainties related to external disturbances and noise appear intermittently. In order to restore the filtering optimality lost due to the conservativeness of the EH ∞ F design, we developed an optimal‐switched (OS) filtering mechanism based on the standard EH ∞ F to obtain an optimal‐switched extended H ∞ filter (OS‐EH ∞ F). The OS mechanism has an error‐tolerant switched (ETS) structure, which switches the filtering mode between optimal and H ∞ robust by setting a switching threshold with redundancy to uncertainties, and a robustness‐optimality cost function (ROCF) is introduced to determine the threshold and optimize the ETS structure online. The ROCF is the weighted sum of the quantified filtering robustness and optimality. When a weight is given, the proposed OS‐EH ∞ F can obtain the optimal state estimates while maintaining the filtering robustness at an invariant ratio. A simulation example of space target tracking has demonstrated the superior estimation performance of the OS‐EH ∞ F compared with some other typical filters, thereby verifying the effectiveness of using the weight to evaluate the estimation result of the filters.