Bipartite Consensus Tracking via Reinforcement-Learning-Based Time-Synchronized Control.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Multiagent systems require coordinated behavior for tasks like distributed sensing or formation control.
• Bipartite consensus tracking involves agents converging to distinct, yet related, states, often with signed interactions.
• Existing control methods may lack adaptability or guaranteed fixed-time convergence in complex topologies.

Purpose of the Study:

• To develop an optimized time-synchronized control (TSC) method for bipartite consensus tracking in multiagent systems.
• To integrate reinforcement learning for adaptive optimization of the control process.
• To ensure fixed-time convergence and prove Bellman optimality within signed directed graph interactions.

Main Methods:

• A time-synchronized sliding mode control (TSC) framework was employed to achieve fixed-time bipartite consensus.
• Reinforcement learning, specifically an actor-critic architecture, was utilized to minimize Bellman residual for optimal control.
• Theoretical analysis was conducted to validate fixed-time convergence and Bellman optimality.

Main Results:

• The proposed TSC method successfully achieved fixed-time bipartite consensus among all follower agents.
• Reinforcement learning adaptively optimized the control process, significantly improving performance.
• The upper bound of the convergence time was theoretically determined by controller parameters.

Conclusions:

• The optimized TSC method effectively ensures fixed-time bipartite consensus in multiagent systems with signed interaction topologies.
• Reinforcement learning integration provides adaptive and optimal control, outperforming traditional methods.
• The study demonstrates a robust approach for complex coordination problems in networked systems.