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.

翻译：本文提出了一种适用于多智能体控制综合的通用框架，该框架可广泛应用于具有收敛保证的各类问题，且不受底层图拓扑复杂性及目标函数显式时间依赖性的限制。该框架系统性地解决了多智能体系统中一个尤为挑战性的问题，即不同智能体间耦合动力学的去中心化，并天然支持多目标机器人学与实时实现。为验证其通用性与有效性，该框架在三个实验中得以实施：时变领航-跟随编队控制、无需任何近似的时变密度函数去中心化覆盖控制（此为长期存在的开放性问题），以及密集环境下的安全编队导航。