This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarantees, including those with time-varying objective functions. The proposed framework achieves decentralization without inducing dynamical coupling among agents, and it naturally supports multi-objective robotics and real-time implementation. To demonstrate its generality and effectiveness, the framework is applied to solve three representative problems, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions without approximations, which is a long-standing open problem, and safe formation navigation in a dense environment.

翻译：本文提出一个面向多智能体控制综合的通用框架，该框架适用于包括时变目标函数在内的广泛问题，并具有收敛性保证。该框架通过不引入智能体间动力学耦合的方式实现去中心化控制，天然支持多目标机器人任务与实时实现。为验证其通用性与有效性，我们将该框架应用于三个代表性问题的求解：时变领航-跟随编队控制、无需近似的时变密度函数去中心化覆盖控制（该问题长期悬而未决），以及密集环境中的安全编队导航。