Refined Dynamic Event-Triggering Cluster Consensus of Multiagent Systems With Fixed/Switching Topology.

Area of Science:

• Control Systems Engineering
• Distributed Systems
• Artificial Intelligence

Background:

• Multiagent systems (MASs) face challenges in achieving coordinated control, especially with complex network topologies.
• Event-triggered control mechanisms aim to reduce communication load by transmitting data only when necessary.

Purpose of the Study:

• To develop a dynamic event-triggering (DET) mechanism for cluster consensus control in MASs.
• To address challenges of fixed/switching topologies and limited state information using output feedback.

Main Methods:

• A refined sampled-data-based DET scheme with dynamic thresholds for intra- and inter-cluster communication.
• Construction of cooperative control protocols using output feedback.
• Derivation of stability criteria using Lyapunov-Krasovskii functionals (LKFs) and matrix inequalities.
• A particle swarm optimization (PSO)-based algorithm for joint control gain and event-trigger parameter calculation.

Main Results:

• Sufficient criteria for asymptotic stability of the error system under the DET-based control strategy were derived.
• The PSO algorithm effectively addressed the nonconvexity issue in output feedback control design.
• The proposed strategy demonstrated effectiveness in achieving cluster consensus control.

Conclusions:

• The developed DET mechanism enhances cluster consensus control in MASs with dynamic topologies.
• The PSO-based design algorithm provides a robust solution for complex control problems.
• The strategy is validated through a satellite formation flying application.