Robust optimal control of regular languages

This paper presents an algorithm for robust optimal control of regular languages under specified uncertainty bounds on the event cost parameters of the 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 the supervised plant and is minimized over the given range of event cost uncertainties. Synthesis of the robust optimal supervisory control policy requires at most n iterations, where n is the number of states of the deterministic finite-state automaton (DFSA) model, generated from the regular language of the unsupervised plant behavior. The computational complexity of the control synthesis method is polynomial in n.