SCIENCE CHINA Information Sciences, Volume 60, Issue 1: 012202(2017) https://doi.org/10.1007/s11432-015-0327-x

Robust state estimation for uncertain linear systems with random parametric uncertainties

Huabo LIU1,2,*, Tong ZHOU1,3
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  • ReceivedMar 1, 2016
  • AcceptedJun 28, 2016
  • PublishedNov 22, 2016


In this paper, we investigate state estimations of a dynamical system with random parametric uncertainties which may arbitrarily affect a plant state-space model. A robust estimator is derived based on expectation minimization of estimation errors. An analytic solution similar to that of the well-known Kalman filter is derived for this new robust estimator which can be realized recursively with a comparable computational complexity. Under some weak assumptions, it is proved that this estimator converges to a stable system, the covariance matrix of estimation errors is bounded, and the estimation is asymptotically unbiased. Numerical simulations show that the obtained robust filter has an estimation accuracy comparable to other robust estimators and can be applied in a wider range.

Funded by

National Natural Science Foundation of China(61174122)

Specialized Research Fund for the Doctoral Program of Higher Education China(20110002110045)

National Natural Science Foundation of China(51361135705)



This work was supported by National Natural Science Foundation of China (Grant Nos. 61174122, 51361135705) and Specialized Research Fund for the Doctoral Program of Higher Education, China (Grant No. 20110002110045).


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