Decentralized supervisory control of discrete event systems with unknown plants: A learning-based synthesis approach

In this paper, we consider automatic synthesis of decentralized supervisor synthesis for uncertain discrete event systems. In particular, we study the case when the uncontrolled plant is unknown a priori. To deal with the unknown plants, we first characterize the co-normality of prefix-closed regular languages and propose formulas for computing the supremal co-normal sublanguages; then sufficient conditions for the existence of decentralized supervisors are given in terms of language co-normality and a learning-based algorithm to synthesize the supervisor automatically is proposed. The correctness and convergence of the algorithms is proved, and its implementation and effectiveness are illustrated through examples.