4.5 Article

Sensor networks with distributed event-triggered scheme for T-S fuzzy system with dissipativity analysis

Journal

EUROPEAN JOURNAL OF CONTROL
Volume 71, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ejcon.2023.100800

Keywords

Distributed extended dissipative filtering; Takagi-Sugeno fuzzy systems; Lyapunov-Krasovskii fuzzy functionals

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This article investigates the problem of distributed extended dissipativity consensus filtering over the mobile sensor environment wherein each sensor communicates through an event. A unified approach is proposed to address the fuzzy filtering problem considering H∞, L2-L∞, and dissipative performance constraints. The event-triggering scheme is determined by event-based data processors (EDPs) to decide whether the sampled-data needs to be transmitted. By transforming it into an error system with an interval time-varying delay, a new fuzzy Lyapunov-Krasovskii approach is derived to solve the distributed event-triggered dissipative consensus filtering problem in the form of Linear Matrix Inequality (LMIs). Finally, a unified algorithm for co-designed filter gains and threshold parameters at the event condition is presented using an engineering example.
In this article, the problem of distributed extended dissipativity consensus filtering over the mobile sensor environment in which each sensor communicates through an event is investigated. We address the fuzzy filtering problem through a unified approach that considers H & INFIN; , L 2 - L & INFIN; and dissipative performance constraints. The event-triggering scheme is determined by event-based data processors (EDPs) , which determine whether the sampled-data needs to be transmitted. A filter built on event data is applied at each node based on the collected samples from a sensor and its neighbors. By transforming it into an error system with an interval time-varying delay, the distributed event-triggered dissipative consensus filtering problem is solved. A new fuzzy Lyapunov-Krasovskii approach is derived to solve the filtering problem in the form of Linear Matrix Inequality (LMIs) . Finally, using an engineering example, a unified algorithm for co-designed filter gains as well as for threshold parameters at the event condition is presented.& COPY; 2023 Published by Elsevier Ltd on behalf of European Control Association.

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