Results 241 to 250 of about 178,500 (285)
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Particle filtering and moving horizon estimation
Computers & Chemical Engineering, 2006This paper provides an overview of currently available methods for state estimation of linear, constrained and nonlinear systems. The following methods are discussed: Kalman filtering, extended Kalman filtering, unscented Kalman filtering, particle filtering, and moving horizon estimation.
James B. Rawlings, Bhavik R. Bakshi
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Moving horizon estimation for switching nonlinear systems
Automatica, 2013This paper is concerned with moving horizon estimation for a class of constrained switching nonlinear systems, where the system mode is regarded as an unknown discrete state to be estimated together with the continuous state. In this work, we establish the observability framework of switching nonlinear systems by proposing a series of concepts about ...
Yafeng Guo, Biao Huang
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Linear Moving Horizon Estimation With Pre-Estimating Observer
IEEE Transactions on Automatic Control, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sui, Dan, Johansen, Tor Arne, Feng, Le
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Zero-Order Moving Horizon Estimation
2019 IEEE 58th Conference on Decision and Control (CDC), 2019Moving Horizon Estimation (MHE) is an optimization-based approach to nonlinear state estimation. The computational burden associated with the online solution of the corresponding nonlinear optimization problems poses a major challenge when applying MHE in practice.
Katrin Baumgartner +2 more
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Distributed moving horizon estimation for sensor networks
IFAC Proceedings Volumes, 2009This paper focuses on distributed state estimation using a sensor network for monitoring a linear system. In order to account for physical constraints on process states and inputs, we propose a moving horizon approach where each sensor has to solve a quadratic programming problem at each time instant.
FARINA, MARCELLO +2 more
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2018
Nearly every model predictive control (MPC) algorithm is premised on knowledge of the system’s state. As a result, state estimation is vital to good MPC performance. Moving horizon estimation (MHE) is an optimization-based state estimation algorithm.
James B. Rawlings, Douglas A. Allan
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Nearly every model predictive control (MPC) algorithm is premised on knowledge of the system’s state. As a result, state estimation is vital to good MPC performance. Moving horizon estimation (MHE) is an optimization-based state estimation algorithm.
James B. Rawlings, Douglas A. Allan
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Moving horizon estimation for hybrid systems
IEEE Transactions on Automatic Control, 2000We propose a state-smoothing algorithm for hybrid systems based on moving-horizon estimation (MHE) by exploiting the equivalence between hybrid systems modeled in the mixed logic dynamical form and piecewise affine systems. We provide sufficient conditions on the time horizon and the penalties on the state at the beginning of the estimation horizon to ...
FERRARI TRECATE, GIANCARLO +2 more
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Moving horizon estimation on a chip
2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014Second order Quadratic Programming (QP) solvers such as interior-point method (IPM) require the solution of a system of linear equations at every iteration and could be a factor limiting the implementation of IPM to miniaturized devices or embedded systems.
Thuy V. Dang, K. V. Ling
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