Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations

By C. V. Rao, James B. Rawlings, David Q. Mayne. IEEE Transactions on Automatic Control, 2003

916 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/054ac1c63d1a9967b705a3c09a5d9575eb944078

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In Control and Systems Engineering

Citations in the corpus

Model predictive control: Recent developments and future promise (2014, methodology): “Moving horizon estimation of the state of a system described by x+ = f (x, u, w), y = h(x)+ v is considered in Rao, Rawlings, and Mayne (2003).”
Ltd Registered in England and Wales Registered Number : 1072954 Registered office : (2004, neutral): “We believe that comparisons of MHE for nonlinear systems (Rao et al. 2003) against ECUKF and MAUKF would be valuable in a deeper analysis of the algorithms and that this should be pursued in the near future.”
Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “Including this term is similar to the inclusion of an arrival cost in moving horizon estimation approaches [76].”
Architectures for distributed and hierarchical Model Predictive Control - A review (2009, methodology): “for this problem by resorting to the ideas underlying the moving horizon estimators described in [66,83,84].”
Applying the unscented Kalman filter for nonlinear state estimation (2008, background): “An other way of nonlinear state estimation with constraints is moving horizon estimation (MHE), in which the constraints can be included in the estimation problem in a natural way [17].”
A tutorial review of economic model predictive control methods (2014, methodology): “As another approach to output feedback EMPC, moving horizon estimation (MHE) based on least squares techniques has become a popular state estimation technique because of its ability to handle nonlinear systems, account for the presence of disturbances, and account for constraints on decision variabl”
A moving horizon approach to Networked Control system design (2004, methodology): “In the sequel, we bring together ideas from MC sampling [20] and moving horizon estimation [18], [34].”
On unscented Kalman filtering with state interval constraints (2010, methodology): “Model information is used during the forecast step, while measurement data are injected into the estimates during the data-assimilation step.”
Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation (2005, background): “showed that MHE is an asymptotically stable observer in a deterministic modeling framework [11, 8].”