A BDD-Based Approach for Modeling Plant and Supervisor by Extended Finite Automata

Abstract

In this paper, we settle some problems that are encountered when modeling and synthesizing complex industrial systems by the supervisory control theory. First, modeling such huge systems with explicit state-transition models typically results in an intractable model. An alternative modeling approach is to use extended finite automata (EFAs), which is an augmentation of ordinary automata with variables. The main advantage of utilizing EFAs for modeling is that more compact models are obtained. The second problem concerns the ease to understand and implement the supervisor. To handle this problem, we represent the supervisor in a modular manner by extending the original EFAs by compact conditional expressions. This will provide a framework for the users where they can both model their system and obtain the supervisor in form of EFAs. In order to be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams (BDDs). All computations that are performed in this framework are based on BDD operations. The framework has been implemented in a supervisory control tool and applied to industrially relevant benchmark problems.