Process-theoretic supervisor synthesis framework with data

Supervisory control theory deals with automated synthesis of supervisory controllers that coordinate high-level system behavior. We present a process theory with data that subsumes existing extensions of the traditional theory with parameters and data. To this end, we revisit the notion of partial bisimulation, which models controllability of nondeterministic systems, while retaining desirable algebraic properties. We illustrate our framework by presenting a parameterized model of a pipeless plant. At the end, we instantiate a concrete model of the plant and synthesize a nonblocking supervisor for it using the synthesis tool Supremica.