期刊
2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
卷 -, 期 -, 页码 2900-2905出版社
IEEE
关键词
Robust Moving Horizon Estimation; Uncertain Systems; Semidefinite programming; LMI optimization
资金
- EPSRC Industrial CASE Studentship award
- Schlumberger [EP/R512540/1]
- EPSRC [2103076] Funding Source: UKRI
This paper investigates the problem of state estimation for linear-time-invariant (LTI) discrete-time systems subject to structured feedback uncertainty and bounded disturbances. The proposed Robust Moving Horizon Estimation (RMHE) scheme computes at each sample time tight bounds on the uncertain states by solving a linear matrix inequality (LMI) optimization problem based on the available noisy input and output data. In comparison with conventional approaches that use offline calculation for the estimation, the suggested scheme achieves an acceptable level of performance with reduced conservativeness, while the online computational time is maintained relatively low. The effectiveness of the proposed estimation method is assessed via a numerical example.
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