Mean Payoff Supervisory Control Under Partial Observation
Mean Payoff Supervisory Control Under Partial Observation
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DOI：
10.1109/cdc.2018.8619193
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Yiding Ji;Xiang Yin;S. Lafortune
中科院分区：
文献类型：
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作者：
Yiding Ji;Xiang Yin;S. Lafortune
所研究的问题是在一个由有限状态加权自动机建模的部分可观测定量离散事件系统上的平均收益监督控制。我们打算设计一个部分观测监督器，使得被监督系统中所有无限序列的极限平均权重保持非负。这个问题可以被看作是监督器和环境之间的一个双人定量博弈，具有不对称信息和一个平均收益目标。为了处理监督器的部分观测问题，我们引入了能量信息状态，它包含了状态估计和能量变化的信息，用于监督器的决策。在此基础上，我们将监督控制问题转化为一个在完全观测下的双人可达性博弈，并提出了一种称为首次循环能量包含控制器（FCEIC）的有限二分结构。进一步的分析表明，FCEIC中的获胜策略可导致原始控制问题的解决方案。
The problem under investigation is mean payoff supervisory control on a partially observed quantitative discrete event system modeled by a finite state weighted automaton. We intend to design a partial-observation supervisor such that the limit-average weights of all infinite sequences in the supervised system remain nonnegative. This problem may be viewed as a two-player quantitative game between the supervisor and the environment, with asymmetric information and a mean payoff objective. To cope with partial observation of the supervisor, we introduce the energy information state which incorporates information about both state estimate and energy change for supervisor's decision making. Based on that, we transfer the supervisory control problem into a two-player reachability game under full observation and propose a finite bipartite structure called First Cycle Energy Inclusive Controller (FCEIC). Further analysis demonstrates that winning strategies in the FCEIC lead to solutions to the original control problem.