| 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. |
| 关键词: Robust estimation Deterministic input Regularized least-squares |
| DOI： |
| Received:May 28, 2014Revised:October 28, 2014 |
| 基金项目: |
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| Robust state estimation for uncertain linear systems with deterministic input signals |
| Huabo LIU,Tong ZHOU |
| (Department of Automation, Tsinghua University; College of Automation Engineering, Qingdao University;Department of Automation, Tsinghua University; Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University) |
| Abstract: |
| 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. |
| Key words: Robust estimation Deterministic input Regularized least-squares |
