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Top-Down Nested Supervisory Control of State-Tree Structures Based on State Aggregations⁎⁎This work was supported in part by the Natural Science Foundation of China under Grant No. 61703322, the Alexander von Humboldt Foundation, the Science and Technology Development Fund, MSAR, under Grant No. 0012/2019/A1, and the Fundamental Research Funds for the Central Universities under Grant Nos. XJS200403 and JBF180401
With a structured state space, state-tree structures (STS) are a powerful framework to model hierarchical finite state machines (HFSM). The boundary consistency property of STS endows them a compact and neatly representation. In this study, by naturally decomposing an STS into a set of STS nests in a top-down nested approach and finding a supervisor for each, the boundary consistency property is extended to the supervisory control of STS. As a consequence, the state spaces for both the system model and optimal supervisor are significantly reduced. Two examples are provided, in which the state space of a large scale HFSM example is reduced from 1024 to 2 × 1018.