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Prescribed performance distributed event-triggered control
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AI
DOI：10.1016/j.automatica.2025.112551.png)
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Abstract
En 中文
Distributed event-triggered control improves resource efficiency by updating control inputs only when necessary. Most existing related studies focus primarily on steady-state performance, often overlooking transient behavior. This paper proposes novel prescribed performance adaptive distributed event-triggered control schemes for networked second-order integrator multi-agent systems, ensuring time-varying consensus even in the presence of disturbances. Specifically, intermittently updated edge-based measurement information is employed for event-triggered feedback control to conserve limited resources. Adaptive gains are designed to eliminate the requirement for global network information during implementation. By incorporating appropriate performance functions, the proposed control schemes guarantee that the relative position and velocity errors satisfy the prescribed transient and steady-state performance bounds. An explicit lower bound on the inter-event intervals within any finite time horizon is derived, revealing the relationship between control performance and triggering frequency. Numerical simulations are provided to demonstrate the effectiveness, superiority, and applicability of the developed control schemes.
Keywords:
distributed event-triggered control
multi-agent systems
prescribed performance
adaptive control
consensus tracking
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W
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