THERMAL SCIENCE

International Scientific Journal

Li Liu

,

Tao Yao

,

Chunjie Zhou

,

Fei Liu

,

Dianli Hou

,

Hongyong Yang

MODELING AND FILTERING FOR A STOCHASTIC UNCERTAIN SYSTEM IN A COMPLEX SCENARIO

ABSTRACT
This paper presents a new approach to filter signals for discrete-time physical problems with stochastic uncertain in the presence of random data transmission delays, out-of-order packets and correlated noise. To deal with the packet disorder, the system model synthesizing the transmission delays and out-of-order packets from the plant to the filter is established by utilizing signal reconstruction schemes based on the zero-order-holder and logic zero-order-holder. A robust finite horizon Kalman filter is proposed by augmenting the state-space model and minimizing the error covariance. To further improve the filtering performance, a linear estimation-based delay compensation strategy is proposed by employing the reorganized time-stamped measurements. Moreover, for solving the missing measurement problem whilst reducing the computational costs, an artificial delay compensation approach is established using an one-step prediction approach. Simulation results show the effectiveness of the proposed method.
KEYWORDS
finite horizon filtering, transmission delays, out-of-order packets, variational principle, fractal calculus
PAPER SUBMITTED: 1970-01-01
PAPER REVISED: 2020-04-26
PAPER ACCEPTED: 2020-04-26
PUBLISHED ONLINE: 2021-01-31
DOI REFERENCE: https://doi.org/10.2298/TSCI191201041L
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 2, PAGES [1411 - 1424]
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Modeling and filtering for a stochastic uncertain system in a complex scenario

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence