Decentralized supervisory control of discrete event systems with unknown plants: A learning-based synthesis approach
Jin Dai, Hai Lin · 2014
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.