Consensus of Second-Order Hybrid Multiagent Systems by Event-Triggered Strategy

Area of Science:

• Control Systems Engineering
• Distributed Systems
• Robotics

Background:

• Multiagent systems (MASs) integrating discrete-time and continuous-time agents present unique control challenges.
• Achieving consensus in such hybrid systems requires careful consideration of inter-agent communication and dynamics.
• Existing consensus protocols may not be efficient for hybrid MASs due to continuous communication overhead.

Purpose of the Study:

• To propose an event-triggered control method for achieving consensus in second-order hybrid MASs.
• To establish criteria for coupling gains, communication topology eigenvalues, and sampling intervals that guarantee hybrid consensus and system stability.
• To demonstrate the effectiveness of the event-triggered strategy in enabling agents to communicate and update controllers only when necessary.

Main Methods:

• Development of an event-triggered control strategy for second-order hybrid MASs.
• Derivation of selection criteria for system parameters (coupling gains, topology eigenvalues, sampling interval) to ensure stability and consensus.
• Mathematical proof of hybrid second-order consensus under the event-triggered framework.
• Validation through simulation examples.

Main Results:

• A novel event-triggered method is presented for hybrid MAS consensus.
• Selection criteria are provided to guarantee stability and consensus in systems with discrete-time and continuous-time agents.
• The event-triggered strategy effectively reduces communication and control updates while maintaining consensus.
• Simulation results confirm the validity and efficacy of the proposed approach.

Conclusions:

• The proposed event-triggered method offers an efficient solution for achieving consensus in second-order hybrid MASs.
• The established criteria ensure system stability despite the complexities of hybrid dynamics.
• This approach minimizes communication load by triggering updates only when essential, outperforming traditional continuous-time methods.