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• W2022088595 abstract "We propose a solution to moving-horizon state estimation that incorporates inequality constraints in both a systematic and computationally efficient way, akin to Kalman filtering. The proposed method allows the on-line constrained optimization problem involved in moving-horizon state estimation to be solved offline, requiring only a look-up table and simple function evaluations for real-time implementation. The method is illustrated via simulations on a system that has been studied in literature." @default.
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• W2022088595 date "2007-05-01" @default.
• W2022088595 modified "2023-10-14" @default.
• W2022088595 title "A parametric programming approach to moving-horizon state estimation" @default.
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• W2022088595 doi "https://doi.org/10.1016/j.automatica.2006.11.021" @default.
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