Adaptive Consensus Control of Linear Multiagent Systems With Dynamic Event-Triggered Strategies.

Area of Science:

• Control Theory
• Networked Systems
• Artificial Intelligence

Background:

• Multiagent systems (MASs) require efficient communication for coordinated behavior.
• Event-triggered control reduces communication load compared to time-triggered systems.
• Adaptive control adjusts system parameters in real-time for improved performance.

Purpose of the Study:

• To develop a dynamic event-triggered consensus strategy for general linear MASs.
• To design an adaptive control protocol that includes dynamic coupling strength adjustment.
• To ensure consensus in both leaderless and leader-following MAS configurations.

Main Methods:

• A distributed dynamic event-triggered strategy with an auxiliary parameter for threshold regulation.
• A distributed adaptive consensus protocol with an updating law for coupling strength.
• Derivation of consensus criteria and proof of non-Zeno behavior for triggering sequences.

Main Results:

• The proposed dynamic event-triggered strategy leads to fewer triggering instants than static methods.
• Guaranteed leaderless and leader-following consensus for general linear MASs.
• Demonstrated absence of Zeno behavior in the triggering time sequences.

Conclusions:

• The dynamic event-triggered control mechanism combined with adaptive control is effective for MAS consensus.
• The proposed strategy offers improved efficiency in communication for networked systems.
• Validated through simulation examples, confirming the practical applicability of the approach.