Distributed event-triggered control for multi-agent systems
Abstract
Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agent controller updates. The controller updates considered here are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem. A centralized formulation is considered first and then its distributed counterpart, in which agents require knowledge only of their neighbors' states for the controller implementation. The results are then extended to a self-triggered setup, where each agent computes its next update time at the previous one, without having to keep track of the state error that triggers the actuation between two consecutive update instants. The results are illustrated through simulation examples.
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Given a connected, undirected graph G_comm and closed-loop dynamics (16), if event times are determined as the times when the triggering condition holds, then the system achieves multi-agent average consensus.
“Given a connected, undirected graph G comm and the closed-loop dynamics (16) , if the event times are determined as the times when”
From Event-triggered communication and control of networked systems for multi-agent consensus (2019) · cites this paper as Dimarogonas et al., 2012
Dimarogonas et al. (2012) are able to rule out the existence of Zeno behavior by showing there exists a positive MIET uniformly bounding the inter-event times.
“Dimarogonas et al. (2012) are able to rule out the existence of Zeno behavior, cf. Definition 2.1, by showing there exists a positive MIET”
From Event-triggered communication and control of networked systems for multi-agent consensus (2019) · cites this paper as Dimarogonas et al., 2012
Theorem 2.6: Given a connected, undirected graph and closed-loop dynamics (22), if event times of each agent i are determined by trigger (27) with 0<a<1/|N_i|, then all non-Zeno trajectories asymptotically achieve multi-agent average consensus.
“then all nonZeno trajectories of the system asymptotically achieve multi-agent average consensus.”
From Event-triggered communication and control of networked systems for multi-agent consensus (2019) · cites this paper as Dimarogonas et al. (2012)
In the derivation of Theorem 2.6, Dimarogonas et al. (2012) only show that at all times there exists one agent i for which the interevent times are strictly positive.
“Instead, in the derivation of the result of Theorem 2.6, Dimarogonas et al. (2012) only show that at all times there exists one agent i for which the interevent times are strictly positive.”
From Event-triggered communication and control of networked systems for multi-agent consensus (2019) · cites this paper as Dimarogonas et al. (2012)
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- Authors
- Dimarogonas, D. V., Frazzoli, E., & Johansson, K. H.
- Affiliations
- KTH Royal Institute of Technology (SE)
- Massachusetts Institute of Technology (US)
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2012-05
- Volume
- 57
- Issue
- 5
- Pages
- 1291-1297
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W2167183308
- Cited by
- 2,556
- References
- 31
- Citations by year
- 2026: 129 · 2025: 198 · 2024: 223 · 2023: 224 · 2022: 251 · 2021: 238 · 2020: 237 · 2019: 246 · 2018: 223 · 2017: 165 · 2016: 153 · 2015: 114 · 2014: 96 · 2013: 47 · 2012: 11
- FWCI
- 64.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Automatic Control (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Neural Networks Stability and Synchronization
- Keywords
- event-triggered control, multi-agent systems, distributed control, self-triggered control, resource-constrained embedded systems
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