Approximate event-triggering approaches

Dawei Shi; Ling Shi; Tongwen Chen

doi:10.1007/978-3-319-26606-0_4

Approximate event-triggering approaches

Dawei Shi^*, Ling Shi, Tongwen Chen

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter, approximate approaches to event-based estimator design are introduced. The approximation techniques are mainly utilized to handle the non-Gaussian distributions caused by the exploitation of the event-triggered measurement information. Although the precision of the approximations on estimation performance cannot be theoretically verified, the benefit is that the computational complexity of the resultant estimators can be greatly reduced, which is helpful in real-time application and implementation of event-based estimation algorithms.

Original language	English
Title of host publication	Studies in Systems, Decision and Control
Publisher	Springer International Publishing
Pages	47-75
Number of pages	29
DOIs	https://doi.org/10.1007/978-3-319-26606-0_4
Publication status	Published - 2016

Publication series

Name	Studies in Systems, Decision and Control
Volume	41
ISSN (Print)	2198-4182
ISSN (Electronic)	2198-4190

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10.1007/978-3-319-26606-0_4

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Shi, D., Shi, L., & Chen, T. (2016). Approximate event-triggering approaches. In Studies in Systems, Decision and Control (pp. 47-75). (Studies in Systems, Decision and Control; Vol. 41). Springer International Publishing. https://doi.org/10.1007/978-3-319-26606-0_4

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AB - In this chapter, approximate approaches to event-based estimator design are introduced. The approximation techniques are mainly utilized to handle the non-Gaussian distributions caused by the exploitation of the event-triggered measurement information. Although the precision of the approximations on estimation performance cannot be theoretically verified, the benefit is that the computational complexity of the resultant estimators can be greatly reduced, which is helpful in real-time application and implementation of event-based estimation algorithms.

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Approximate event-triggering approaches

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