Optimal Threshold-Based Distributed Control Policies for Persistent Monitoring on Graphs
Optimal Threshold-Based Distributed Control Policies for Persistent Monitoring on Graphs
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DOI：
10.23919/acc.2019.8814440
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发表时间：
2019-07
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通讯作者：
Nan Zhou;C. Cassandras;Xi Yu;S. Andersson
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作者：
Nan Zhou;C. Cassandras;Xi Yu;S. Andersson
我们考虑了最优多智能体持久监控问题，该问题由一组协作智能体访问图上的一组节点(目标)，目标是最小化总体节点状态不确定性的度量。这个问题的解决方案涉及由每个代理要访问的节点序列和在每个节点上花费的时间量定义的代理轨迹。我们提出了一类基于阈值的分布式参数控制器，通过节点不确定性的阈值来控制代理从一个节点到下一个节点的转移。由此产生的主体-目标系统的行为被描述为混合动态系统。这使得可以使用无穷小扰动分析(IPA)通过梯度下降来在线确定最优阈值参数，从而在这类基于阈值的策略中获得最优控制器。通过模拟来说明我们的结果，并将它们与通过动态规划得到的最优解进行比较。
We consider the optimal multi-agent persistent monitoring problem defined by a team of cooperating agents visiting a set of nodes (targets) on a graph with the objective of minimizing a measure of overall node state uncertainty. The solution to this problem involves agent trajectories defined both by the sequence of nodes to be visited by each agent and the amount of time spent at each node. We propose a class of distributed threshold-based parametric controllers through which agent transitions from one node to the next are controlled by thresholds on the node uncertainty. The resulting behavior of the agent-target system is described by a hybrid dynamic system. This enables the use of Infinitesimal Perturbation Analysis (IPA) to determine on-line optimal threshold parameters through gradient descent and thus obtain optimal controllers within this family of threshold-based policies. Simulations are included to illustrate our results and compare them to optimal solutions derived through dynamic programming.