Quantifying model quality for supervisory control synthesis - an experimental study

Abstract

Abstract Supervisory control synthesis is a model-based engineering method to design supervisory controllers for high-tech and cyber-physical systems. Recent advances in synthesis techniques and modelling formalisms allow for synthesis of supervisors for large-scale industrial applications. Yet, the synthesis results depends on the quality and validity of the models used as input. Other model-based techniques such as simulation, testing, and verification provide complementary support in the design process to increase the quality and validity of the models. In this paper, we propose, in addition to the other supporting techniques, eleven modeling aspects to assess the model quality in the context of supervisory control synthesis. Examples of modeling aspects are the interdependency between component models, whether independent subsystems are modeled, and whether the model is annotated with comments. For each modeling aspect, we discuss its importance and describe how it can be quantified. We report on an experiment where 21 models of automated guided vehicles, created by students during a course on Supervisory Control Theory, are evaluated with the proposed modeling aspects. This experiment demonstrates the applicability of the modeling aspects.