Despite the correct-by-construction property, one of the major drawbacks of supervisory control synthesis is state-space explosion. Several approaches have been proposed to overcome this computational difficulty, such as modular, hierarchical, decentralized, and multilevel supervisory control synthesis. Unfortunately, the modeler needs to provide additional information about the system's structure or controller's structure as input for most of these nonmonolithic synthesis procedures. Multilevel synthesis assumes that the system is provided in a tree-structured format, which may resemble a system decomposition. In this paper, we present a systematic approach to transform a set of plant models and a set of requirement models provided as extended finite automata into a tree-structured multilevel discrete-event system to which multilevel supervisory control synthesis can be applied. By analyzing the dependencies between the plants and the requirements using dependence structure matrix techniques, a multilevel clustering can be calculated. With the modeling framework of extended finite automata, plant models and requirements depend on each other when they share events or variables. We report on experimental results of applying the algorithm's implementation on several models available in the literature to assess the applicability of the proposed method. The benefit of multilevel synthesis based on the calculated clustering is significant for most large-scale systems.

中文翻译：

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使用依赖结构矩阵构建多级离散事件系统


尽管具有构造正确性，但监督控制综合的主要缺点之一是状态空间爆炸。已经提出了几种方法来克服这种计算困难，例如模块化、分层、分散和多级监督控制综合。不幸的是，建模者需要提供有关系统结构或控制器结构的附加信息，作为大多数非整体综合过程的输入。多级综合假设系统以树形结构格式提供，这可能类似于系统分解。在本文中，我们提出了一种系统方法，将一组工厂模型和一组作为扩展有限自动机提供的需求模型转换为可以应用多级监督控制综合的树形结构多级离散事件系统。通过使用依赖结构矩阵技术分析工厂和需求之间的依赖关系，可以计算多级聚类。利用扩展有限自动机的建模框架，工厂模型和需求在共享事件或变量时相互依赖。我们报告了在文献中可用的几个模型上应用该算法实现的实验结果，以评估所提出方法的适用性。对于大多数大型系统来说，基于计算聚类的多级综合的好处是显着的。