We propose a unified framework to fast generate a safe optimal control action for a new task from existing controllers on Multi-Agent Systems (MASs). The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task. Instead of sophisticatedly tuning the cost parameters and other hyper-parameters for safe and reliable behavior in the optimal control framework, the safety of each single task solution is guaranteed using the control barrier functions (CBFs) for high-degree stochastic systems, which constrains the system state within a known safe operation region where it originates from. Linearity of CBF constraints in control enables the control action composition. The discussed framework can immediately provide reliable solutions to new tasks by taking a weighted mixture of solved component-task actions and filtering on some CBF constraints, instead of performing an extensive sampling to achieve a new controller. Our results are verified and demonstrated on both a single UAV and two cooperative UAV teams in an environment with obstacles.

中文翻译：

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网络化多代理系统上安全最优控制动作的推广

我们提出了一个统一的框架，以从多代理系统 (MAS) 上的现有控制器为新任务快速生成安全的最佳控制动作。控制动作组合是通过根据每个组件任务的贡献对现有控制器进行加权混合来实现的。不是在最优控制框架中为安全可靠的行为复杂地调整成本参数和其他超参数，而是使用高度随机系统的控制屏障函数 (CBF) 来保证每个单个任务解决方案的安全性，这限制了系统状态在其起源的已知安全操作区域内。控制中 CBF 约束的线性使得控制动作组合成为可能。所讨论的框架可以通过采用已解决的组件任务操作的加权混合和过滤某些 CBF 约束，而不是执行广泛的采样来实现新的控制器，从而立即为新任务提供可靠的解决方案。我们的结果在有障碍物的环境中在单个无人机和两个合作无人机团队上得到验证和证明。