论文标题
事件驱动的向后水平控制控制网络系统中的分布式持续监控
Event-Driven Receding Horizon Control For Distributed Persistent Monitoring in Network Systems
论文作者
论文摘要
我们解决了在网络拓扑上互连的一组节点（目标）上定义的多代理持续监视问题。在有限时期评估的平均节点状态不确定性的量度应通过控制合作的代理团队的运动来最大程度地减少。为了解决这个问题，我们提出了一种事件驱动的后退视野控制方法，该方法在计算上有效，分布式和在线。所提出的控制器不同于现有的基于在线梯度的参数控制器和离线贪婪循环搜索方法，这些方法通常会导致表现较低的本地Optima或计算密集的集中式解决方案。该控制器中的一个关键小说元素是它会自动优化其计划范围的长度，从而使其无参数。我们表明，在调用退化的地平线控制器的每个事件中遇到的每个分布式优化问题可以获得明确的全球最佳解决方案。与在线参数控制解决方案分布的最新情况相比，提供了数值结果。
We address the multi-agent persistent monitoring problem defined on a set of nodes (targets) interconnected over a network topology. A measure of mean overall node state uncertainty evaluated over a finite period is to be minimized by controlling the motion of a cooperating team of agents. To address this problem, we propose an event-driven receding horizon control approach that is computationally efficient, distributed and on-line. The proposed controller differs from the existing on-line gradient-based parametric controllers and off-line greedy cycle search methods that often lead to either low-performing local optima or computationally intensive centralized solutions. A critical novel element in this controller is that it automatically optimizes its planning horizon length, thus making it parameter-free. We show that explicit globally optimal solutions can be obtained for every distributed optimization problem encountered at each event where the receding horizon controller is invoked. Numerical results are provided showing improvements compared to state of the art distributed on-line parametric control solutions.
