Efficient Symbolic Supervisory Synthesis and Guard Generation: Evaluating partitioning techniques for the state-space exploration
Paper in proceedings, 2011

The supervisory control theory (SCT) is a model-based framework, which automatically synthesizes a supervisor that restricts a plant to be controlled based on specifications to be fulfilled. Two main problems, typically encountered in industrial applications, prevent SCT from having a major breakthrough. First, the supervisor which is synthesized automatically from the given plant and specification models might be incomprehensible to the users. To tackle this problem, an approach was recently presented to extract compact propositional formulae (guards) from the supervisor, represented symbolically by binary decision diagrams (BDD). These guards are then attached to the original models, which results in a modular and comprehensible representation of the supervisor. However, this approach, which computes the supervisor symbolically in the conjunctive way, might lead to another problem: the state-space explosion, because of the large number of intermediate BDD nodes during computation. To alleviate this problem, we introduce in this paper an alternative approach that is based on the disjunctive partitioning technique, including a set of selection heuristics. Then this approach is adapted to the guard generation procedure. Finally, the efficiency of the presented approach is demonstrated on a set of benchmark examples.

symbolic representation

reachability search

deterministic finite automata

propositional formula

Supervisory control theory


Zhennan Fei

Chalmers, Signals and Systems, Systems and control, Automation

Sajed Miremadi

Chalmers, Signals and Systems, Systems and control, Automation

Knut Åkesson

Chalmers, Signals and Systems, Systems and control, Automation

Bengt Lennartson

Chalmers, Signals and Systems, Systems and control, Automation

ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

Vol. 1 106-115

Areas of Advance


Subject Categories

Information Science

Computer Science



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