Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/22989
Title: | Distributed state and fault estimation over sensor networks with probabilistic quantizations: The dynamic event-triggered case |
Authors: | Li, Q Wang, Z Hu, J Sheng, W |
Keywords: | Sensor networks;State and fault estimation;Distributed estimation;Dynamic event-triggered mechanisms;Probabilistic quantizations |
Issue Date: | 29-Jun-2021 |
Publisher: | Elsevier |
Citation: | Li, Q., Wang, Z., Hu, J. and Sheng, W. (2021) 'Distributed state and fault estimation over sensor networks with probabilistic quantizations: The dynamic event-triggered case', Automatica, 131, pp. 109784. doi: https://doi.org/10.1016/j.automatica.2021.109784. |
Abstract: | In this paper, the distributed state and fault estimation problem is discussed for a class of nonlinear time-varying systems with probabilistic quantizations and dynamic event-triggered mechanisms. To reduce resource consumption, a dynamic event-triggered strategy is exploited to schedule the data communication among sensor nodes. In addition, the measurement signals are quantized and then transmitted through the network, where the probabilistic quantizations are taken into consideration. Attention is focused on the problem of constructing a distributed estimator such that both the plant state and the fault signal are estimated simultaneously. By using the matrix difference equation method, certain upper bound on the estimation error covariance is first guaranteed and then minimized at each iteration by properly designing the estimator parameters. Subsequently, for the proposed distributed estimation algorithm, the estimator performance is analyzed and a sufficient condition is established to guarantee that the estimation error is exponentially bounded in mean-square sense. Finally, an illustrative example is provided to verify the usefulness of the developed estimation scheme. |
URI: | http://bura.brunel.ac.uk/handle/2438/22989 |
DOI: | http://dx.doi.org/10.1016/j.automatica.2021.109784 |
ISSN: | 0005-1098 http://dx.doi.org/10.1016/j.automatica.2021.109784 http://dx.doi.org/10.1016/j.automatica.2021.109784 |
Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | 542.22 kB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.