| 摘要: |
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| DOI： |
| Received:June 15, 2014Revised:July 11, 2014 |
| 基金项目: |
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| A learning-based synthesis approach todecentralized supervisory control of discrete event systems with unknown plants |
| J. Dai,H. Lin |
| (Department of Electrical Engineering, University of Notre Dame) |
| Abstract: |
| In this paper, we consider the problem of automatic synthesis of decentralized supervisor 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 conormality of prefix-closed regular languages and propose formulas for computing the supremal
conormal sublanguages; then sufficient conditions for the existence of decentralized supervisors are given in terms of language
controllability and conormality and a learning-based algorithm to synthesize the supervisor automatically is proposed. Moreover,
the paper also studies the on-line decentralized supervisory control of concurrent discrete event systems that are composed of
multiple interacting unknown modules. We use the concept of modular controllability to characterize the necessary and sufficient
conditions for the existence of the local supervisors, which consist of a set of local supervisor modules, one for each plant module
and which determines its control actions based on the locally observed behaviors, and an on-line learning-based local synthesis
algorithm is also presented. The correctness and convergence of the proposed algorithms are proved, and their implementation
are illustrated through examples. |
| Key words: Discrete-event systems Supervisor synthesis Regular language learning Controllability Decentralized control |
