Local Mean Payoff Supervisory Control for Discrete Event Systems
Local Mean Payoff Supervisory Control for Discrete Event Systems
复制标题
离散事件系统的局部平均支付监督控制
DOI：
10.1109/tac.2021.3075186
复制
发表时间：
2021-04
影响因子：
6.8
通讯作者：
Stephane Lafortune
中科院分区：
文献类型：
--
作者：
Yiding Ji;Xiang Yin;Stephane Lafortune
本文研究了建模为加权自动机的离散事件系统上具有局部平均收益目标的定量监督控制。当新事件发生时会生成权重流，需要满足一些定量条件。我们关注有限数量事件的平均权重（收益），它可以作为权重流稳定性或鲁棒性的衡量标准。评估平均收益的事件范围称为窗口，该窗口随着新事件的发生而滑动。除了定量要求之外，安全性和活性等定性要求也是必要的。采用监督控制来操纵系统的运行以满足要求。我们根据窗口大小是否固定来考虑两种不同的场景。相应地，我们提出了两个监督控制问题，这两个问题都通过先解决定性问题，然后解决定量问题来顺序解决。然后，自动机模型被转换为监管者和环境之间的两人游戏，其中安全性和活跃性得到加强。根据中间结果，定义了几个定量目标来制定两个博弈，这两个博弈对应于所提出的两个监督控制问题。最后，我们通过解决博弈综合了可证明正确的监督者，并彻底解决了这两个问题。
This article investigates quantitative supervisory control with local mean payoff objectives on discrete event systems modeled as weighted automata. Weight flows are generated as new events occur, which are required to satisfy some quantitative conditions. We focus on mean weights (payoffs) over a finite number of events, which serve as a measure for the stability or robustness of weight flows. The range of events to evaluate the mean payoff is termed a window, which slides as new events occur. Qualitative requirements such as safety and liveness are also necessary along with quantitative requirements. Supervisory control is employed to manipulate the operation of the system so that the requirements are satisfied. We consider two different scenarios based on whether the window size is fixed or not. Correspondingly, we formulate two supervisory control problems, both of which are solved sequentially by first tackling the qualitative issues and then the quantitative ones. The automaton model is then transformed to a two-player game between the supervisor and the environment, where safety and liveness are enforced. Based on the intermediate results, several quantitative objectives are defined to formulate two games, which correspond to the two proposed supervisory control problems. Finally, we synthesize provably correct supervisors by solving the games and completely resolve both problems.
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影响因子：
6.8
作者：
Shaolong Shu;F. Lin
通讯作者：
Shaolong Shu;F. Lin
DOI：
10.1007/978-0-387-68612-7
发表时间：
2008
期刊：
--
影响因子：
--
作者：
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通讯作者：
C. Cassandras;S. Lafortune
影响因子：
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作者：
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通讯作者：
Yunfeng Hou;Weilin Wang;Yanwei Zang;F. Lin;Miao Yu;Chaohui Gong
DOI：
10.1016/j.ifacol.2018.06.340
发表时间：
2018
期刊：
--
影响因子：
--
作者：
A. Rashidinejad;M. Reniers;Lei Feng
通讯作者：
A. Rashidinejad;M. Reniers;Lei Feng
DOI：
10.1109/cdc40024.2019.9029301
发表时间：
2019-12
期刊：
2019 IEEE 58th Conference on Decision and Control (CDC)
影响因子：
--
作者：
Yiding Ji;Xiang Yin;S. Lafortune
通讯作者：
Yiding Ji;Xiang Yin;S. Lafortune