Asymptotic consensus of hybrid multi-agent systems considering network attacks.

Area of Science:

• Control Theory
• Networked Systems
• Artificial Intelligence

Background:

• Hybrid multi-agent systems face challenges in achieving consensus under network attacks.
• Byzantine nodes pose a significant threat to system integrity and control.
• Existing defense mechanisms may not adequately address complex network attack scenarios.

Purpose of the Study:

• To develop an active defense strategy for hybrid multi-agent systems facing network attacks.
• To achieve asymptotic consensus control in the presence of Byzantine nodes.
• To establish a robust framework for secure and stable multi-agent coordination.

Main Methods:

• Established a deep dynamic heterogeneous redundancy architecture based on mimic defense theory.
• Proposed a hybrid multi-agent asymptotic consensus control method using Lyapunov asymptotic stability theory.
• Developed a hybrid multi-agent progressive consensus clustering review strategy.

Main Results:

• Proved sufficient and necessary conditions for achieving asymptotic consensus using the proposed clustering review strategy.
• Demonstrated the effectiveness of the strategy in ensuring system stability and consensus.
• Validated the theoretical findings through numerical examples.

Conclusions:

• The proposed mimic defense architecture and consensus strategy effectively address active defense and asymptotic consensus control in hybrid multi-agent systems.
• The methodology provides a robust solution for achieving reliable coordination under network attacks.
• The strategy is extendable to proportional consensus for diverse multi-agent tasks.