Robust state estimation for uncertain linear systems with deterministic input signals
Huabo LIU
Tong ZHOU
摘要：In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.
机标关键词：input signalsuncertain linear systemsparametric uncertaintiescomputational complexityparameter uncertaintiescovariance matrixstate estimationrobust estimator
资助基金：This work was supported in part by the 973 Program()This work was supported in part by the 973 Program(2009CB320602)This work was supported in part by the 973 Program(2012CB316504)in part by the National Natural Science Foundation of China()in part by the National Natural Science Foundation of China(61174122)in part by the National Natural Science Foundation of China(61021063)in part by the National Natural Science Foundation of China(60721003)in part by the National Natural Science Foundation of China(60625305)in part by the Specialized Research Fund for the Doctoral Program of Higher Education, China(20110002110045)
论文发表日期：2014-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 383-392 )
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