Robust State Estimation for Uncertain Discrete Linear Systems with Delayed Measurements

Zhijun Li, Minxing Sun, Qianwen Duan and Yao Mao
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Zhijun Li: Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
Minxing Sun: Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
Qianwen Duan: Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
Yao Mao: Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China

Mathematics, 2022, vol. 10, issue 9, 1-24

Abstract: Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addressing the simultaneous existence of random model parametric uncertainties and constant measurement delay in the discrete-time linear systems, this study proposes a novel robust estimation method based on the combination of Kalman filter regularized least-squares (RLS) framework and state augmentation. The state augmentation method is elaborately designed, and the cost function is improved by considering the influence of modelling errors. A recursive program similar to the Kalman filter is derived. Meanwhile, the asymptotic stability conditions of the proposed estimator and the boundedness conditions of its error covariance are analyzed theoretically. Numerical simulation results show that the proposed method has a better processing capability for measurement delay and better robustness to model parametric uncertainties than the Kalman filter based on nominal parameters.

Keywords: constant measurement delay; random parametric uncertainties; state augmentation; robust state estimation; regularized least squares (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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