Disentangled Control of Multi-Agent Systems

2025-11-08

AI Summary

Coordinated control in multi-agent systems is challenged by dynamic couplings arising from time-varying interactions and environmental constraints. Method: This paper proposes a general distributed decoupling control framework that integrates decoupling control theory, distributed optimization, and real-time feedback, underpinned by dynamic graph theory and non-autonomous system analysis. Contribution/Results: It achieves, for the first time, approximation-free distributed coverage control under time-varying density functions—resolving a long-standing open problem. The framework accommodates arbitrary time-varying communication topologies and objective functions, ensuring fully decentralized decision-making and provably strict convergence. Experimental validation spans three canonical scenarios: time-varying leader–follower formation, approximation-free coverage control, and safety-aware navigation in dense environments. It unifies formation control, area coverage, and obstacle avoidance under dynamic conditions, demonstrating both theoretical rigor and real-time engineering feasibility.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarantees, regardless of the complexity of the underlying graph topology and the explicit time dependence of the objective function. The proposed framework systematically addresses a particularly challenging problem in multi-agent systems, i.e., decentralization of entangled dynamics among different agents, and it naturally supports multi-objective robotics and real-time implementations. To demonstrate its generality and effectiveness, the framework is implemented across three experiments, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions without any approximations, which is a long-standing open problem, and safe formation navigation in dense environments.

Problem

• Develops a general framework for multi-agent control synthesis
• Addresses decentralization of entangled dynamics among different agents
• Solves decentralized coverage control for time-varying density functions

Innovation

• Decentralizes entangled dynamics for multi-agent systems
• Guarantees convergence across complex graph topologies
• Supports multi-objective robotics and real-time implementation