Data-driven event-triggered consensus for unknown multi-agent systems: A scalable fully distributed protocol with noisy data

doi:10.1016/j.knosys.2026.116715

Data-driven event-triggered consensus for unknown multi-agent systems: A scalable fully distributed protocol with noisy data

2026-07-01

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DOI：10.1016/j.knosys.2026.116715

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Despite the widespread application of event-triggered control in multi-agent systems, its dependence on precise system models and global network information limits its practicality in the presence of unknown dynamics and communication constraints. This paper studies the consensus issue for continuous-time multi-agent systems with unknown dynamics and proposes a scalable fully distributed event-triggered protocol that uses only the local state information of each agent to ensure consensus among all agents from noisy data. To avoid continuous communication and online model identification, offline collected system operational data are used to directly estimate the dynamic evolution patterns, thereby predicting state changes during non-triggered intervals. Furthermore, a new algorithm is introduced to handle bounded noise interference during data collection, which maintains consensus while exhibiting robustness and scalability. Finally, the validation of the proposed approach is verified through simulations in the scenarios of noise-free, noisy conditions, and with the addition of extra agents.

Keywords:

Data-driven control

Event-triggered mechanism

Fully distributed control

Multi-agent systems

Data-driven event-triggered consensus for unknown multi-agent systems: A scalable fully distributed protocol with noisy data

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