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A robust state estimation for uncertain linear systems with deterministic input signals. (English) Zbl 1340.93181

Summary: 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.

MSC:

93E10 Estimation and detection in stochastic control theory
93E11 Filtering in stochastic control theory
93C05 Linear systems in control theory
93B35 Sensitivity (robustness)
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References:

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