标题： 监督控制器综合的概述与Eclipse ESCET v4.0的性能评估 显示英文标题

标题： Overview and Performance Evaluation of Supervisory Controller Synthesis with Eclipse ESCET v4.0

摘要： 监督控制器控制信息物理系统以确保其正确和安全的操作。 基于综合的工程（SBE）是一种大幅自动化其设计和实现的方法。 SBE将基于模型的工程与计算机辅助设计相结合，使工程师能够专注于系统应该做什么（需求），而不是如何去做（设计和实现）。 在Eclipse监督控制工程工具包（ESCET）开源项目中，用户、研究人员和工具供应商共同开发了一个工具包，以支持整个SBE过程，特别是通过CIF建模语言和工具。 在本文中，我们首先描述CIF的符号监督控制器综合算法，并包括文献中常被忽略但具有重要实际相关性的方面，例如防止运行时错误、处理不同类型的需求以及支持输入变量（以连接外部输入）。 其次，我们介绍并描述CIF的基准模型，这是一个包含23个免费可用的工业和学术模型的集合，这些模型大小和复杂度各不相同。 第三，我们描述了ESCET版本v0.8（2022年12月）和v4.0（2024年6月）之间的最新改进，这些改进影响综合性能，在我们的基准模型上对其进行评估，并展示CIF当前的实际综合性能。 第四，我们简要探讨多级综合，这是一种非整体的综合方法，评估其优势，并表明虽然它可以帮助进一步提高综合性能，但仍需要进一步的性能改进以综合复杂模型。 显示英文摘要

摘要： Supervisory controllers control cyber-physical systems to ensure their correct and safe operation. Synthesis-based engineering (SBE) is an approach to largely automate their design and implementation. SBE combines model-based engineering with computer-aided design, allowing engineers to focus on 'what' the system should do (the requirements) rather than 'how' it should do it (design and implementation). In the Eclipse Supervisory Control Engineering Toolkit (ESCET) open-source project, a community of users, researchers, and tool vendors jointly develop a toolkit to support the entire SBE process, particularly through the CIF modeling language and tools. In this paper, we first provide a description of CIF's symbolic supervisory controller synthesis algorithm, and thereby include aspects that are often omitted in the literature, but are of great practical relevance, such as the prevention of runtime errors, handling different types of requirements, and supporting input variables (to connect to external inputs). Secondly, we introduce and describe CIF's benchmark models, a collection of 23 freely available industrial and academic models of various sizes and complexities. Thirdly, we describe recent improvements between ESCET versions v0.8 (December 2022) and v4.0 (June 2024) that affect synthesis performance, evaluate them on our benchmark models, and show the current practical synthesis performance of CIF. Fourthly, we briefly look at multi-level synthesis, a non-monolithic synthesis approach, evaluate its gains, and show that while it can help to further improve synthesis performance, further performance improvements are still needed to synthesize complex models.