Distributed Event-Triggered Consensus Control of Discrete-Time Linear Multi-Agent Systems under LQ Performance Constraints
Researchers propose an event-triggered protocol that slashes communication needs while guaranteeing performance.
A new research paper by Shumpei Nishida and Kunihisa Okano, published on arXiv, tackles a core challenge in coordinating autonomous systems: communication overhead. The paper, titled "Distributed Event-Triggered Consensus Control of Discrete-Time Linear Multi-Agent Systems under LQ Performance Constraints," introduces a control scheme where agents (like drones or robots) only communicate with neighbors when necessary, based on a local "triggering" rule. This is a shift from traditional methods requiring constant data exchange, which drains bandwidth and battery life.
The proposed method doesn't just save energy—it guarantees performance. The researchers designed it to satisfy a strict Linear Quadratic (LQ) performance constraint, ensuring the system's overall control cost stays within a prescribed bound relative to a baseline continuous-communication system. They provide a sufficient mathematical condition for consensus (all agents agreeing on a state) and develop a tractable, offline method for designing the triggering parameters. Numerical simulations validate the approach, showing it can drastically cut communication events without sacrificing the quality of coordinated action.
This work is significant for real-world deployments where resources are limited. By moving from time-triggered to event-triggered control, systems can operate longer and more reliably. The framework is general, applying to any linear agents connected over an undirected network graph, making it a foundational advance for scalable multi-agent intelligence.
- Uses event-triggered communication, reducing data exchange by ~90% vs. constant 'all-time' baselines.
- Guarantees system-wide consensus and meets a strict Linear Quadratic (LQ) performance constraint.
- Provides a practical, offline design method for selecting local triggering parameters, enabling real-world implementation.
Why It Matters
Enables longer-lasting, more scalable autonomous swarms for logistics, surveillance, and distributed sensing by drastically cutting communication needs.