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

TL;DR

This paper reviews the Eclipse ESCET v4.0 toolkit for supervisory controller synthesis, detailing its algorithms, benchmark models, recent performance improvements, and exploring multi-level synthesis to enhance handling complex models.

Contribution

It provides a comprehensive description of CIF's synthesis algorithm, introduces benchmark models, evaluates recent performance improvements, and explores multi-level synthesis approaches.

Findings

CIF's synthesis algorithm includes practical features like runtime error prevention.

Benchmark models cover a wide range of industrial and academic cases.

Recent ESCET improvements significantly enhance synthesis performance.

Abstract

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.