Suboptimal receding horizon estimation via noise blocking

DOI10.1016/j.automatica.2018.09.012zbMath1406.93324OpenAlexW2891321507WikidataQ129248087 ScholiaQ129248087MaRDI QIDQ1716573

Salah Sukkarieh, He Kong

Publication date: 5 February 2019

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2018.09.012

zbMATH Keywords

state estimationoptimal estimationleast-squares estimationconstrained estimationrecursive estimation

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05) Least squares and related methods for stochastic control systems (93E24) Stochastic stability in control theory (93E15)

Related Items (8)

The noise covariances of linear Gaussian systems with unknown inputs are not uniquely identifiable using autocovariance least-squares ⋮ Moving horizon estimation over relay channels: dealing with packet losses ⋮ An internal model approach to estimation of systems with arbitrary unknown inputs ⋮ Filtering for systems subject to unknown inputs without a priori initial information ⋮ Metamorphic moving horizon estimation ⋮ Kalman filtering under unknown inputs and norm constraints ⋮ Robust distributed model predictive control of linear systems: analysis and synthesis ⋮ A proximity moving horizon estimator for a class of nonlinear systems

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