Robust optimal control of regular languages

This paper presents an algorithm for robust optimal control of regular languages given uncertainty in event costs of a language measure that has been recently reported in literature. The performance index for the proposed robust optimal policy is obtained by combining the measure of the supervised plant language with uncertainty. The performance of a controller is represented by the language measure of supervised plant, minimized over the given range of event cost uncertainties. Synthesis of the robust optimal control policy requires at most n iterations, where n is the number of states of the deterministic finite state automata (DFSA) model generated from the regular language of the open loop plant behavior. The computational complexity of control synthesis is polynomial in n.