Distributed Event-Triggered Adaptive Control for Consensus of Linear Multi-Agent Systems with External Disturbances.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Robotics

Background:

• Linear multi-agent systems face challenges in achieving consensus due to external disturbances.
• Distributed control strategies are crucial for scalable and robust system coordination.
• Event-triggered control offers potential efficiency gains over traditional time-triggered approaches.

Purpose of the Study:

• To develop distributed event-triggered and self-triggered adaptive control strategies for linear multi-agent systems.
• To solve the consensus problem under external disturbances in a fully distributed manner.
• To eliminate the need for continuous monitoring in self-triggered control and prevent Zeno behavior.

Main Methods:

• Design of a distributed event-triggered adaptive output feedback control strategy.
• Development of a distributed self-triggered adaptive output feedback control strategy.
• Analysis to prove consensus achievement and absence of Zeno behavior for both strategies.
• Validation using a two-mass-spring system simulation.

Main Results:

• The proposed event-triggered control strategy enables consensus for connected undirected graphs without global information.
• The self-triggered strategy reduces monitoring requirements while maintaining consensus.
• Both control strategies are proven to prevent Zeno behavior in agents.
• Simulations demonstrate the effectiveness of the proposed control methods.

Conclusions:

• Distributed event-triggered and self-triggered adaptive control are effective for solving consensus problems in linear multi-agent systems with disturbances.
• These strategies offer efficient and robust coordination without requiring global system information or continuous monitoring.
• The developed methods provide a foundation for advanced distributed control applications.