Modular Supervisory Control and Hierarchical Supervisory Control of Fuzzy Discrete-Event Systems
Bibliographic record
Abstract
This paper establishes modular and hierarchical supervisory control theories of Fuzzy Discrete-Event Systems (FDES). It aims to resolve the horizontal and vertical complexities present in large-scale event-driven systems, which are affected by uncertainties in their event and state representations. The modular supervisory control architecture composed of a set of noncommunicating local supervisors, in which one supervisor is assigned for each module having its own sensing and acting capabilities. The notion of separability for languages in FDES is introduced and the property of a language specification of FDES, termed as separably-controllable-observability, is proposed to determine the existence of modular supervisors to the control problem. The hierarchical supervisory control architecture consists of multilevel supervisors assigned to detailed low-level and abstract high-level models of the plant. The notion of output-control-consistency is introduced for languages in FDES. Then, the property called strictly-output-control-consistency is defined for FDES in order to maintain the hierarchical consistency between low-level and high-level FDES modules. The property of H-fuzzy observability is introduced to ensure the hierarchical consistency under the partial observation of low-level FDES. Finally, using the established hierarchical supervisory control theory of FDES, a behavior-based mobile robot navigation example is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".