Moving Horizon Estimation for Nonlinear Networked Control Systems with Unsynchronized Timescales

Philipp, Peter; Lohmann, Boris

doi:10.3182/20110828-6-it-1002.02506

Cited by 1 publication

(1 citation statement)

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“…A few studies in this direction have been reported in the literature. The use of time-stamps on the measurements is proposed in [20] in order to effectively manage the moving horizon data buffer when there is asynchronous sampling and lost and delayed data that may arrive out of sequence. In [21], it is proposed a NMHE strategy that handles dropped measurement packets by choosing the window size different at each sample to ensure that a sufficient (constant) number of measurement packets are used in each state estimate update.…”

Section: Introductionmentioning

confidence: 99%

Regularized Nonlinear Moving Horizon Observer for Detectable Systems

Sui

Johansen

2010

IFAC Proceedings Volumes

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Moving horizon estimation provides a general method for state estimation with strong theoretical convergence properties under the critical assumption that global solutions are found to the associated nonlinear programming problem at each sampling instant. A particular benefit of the approach is due to the use of a moving window of data that is used to update the estimate at each sampling instant. This provides robustness to temporary data deficiencies such as lack of excitation and measurement noise, and the inherent robustness can be further enhanced by introducing regularization mechanisms. In this paper we study moving horizon estimation in cases when output measurements are lost or delayed, which is a common situation when digitally coded data are received over low quality communication channels or random access networks. Modifications to a basic moving horizon state estimation algorithm and conditions for exponential convergence of the estimation errors are given, and the method is illustrated by using a simulation example and experimental data from an offshore oil drilling operation.

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Section: Introductionmentioning

confidence: 99%