Failsafe mechanism design of multicopters based on supervisory control theory
Bibliographic record
Abstract
In order to handle undesirable failures of a multicopter, which occurs in either the pre‐flight process or the in‐flight process, a failsafe mechanism design method based on supervisory control theory (SCT) is proposed for the semi‐autonomous control mode. The failsafe mechanism is a control logic that guides what subsequent actions the multicopter should take, by taking account of real‐time information from guidance, attitude control, diagnosis and other low‐level subsystems. In order to design a failsafe mechanism for the multicopters, safety issues of the multicopters are introduced. Then, user requirements including functional requirements and safety requirements are textually described, where functional requirements guide the modelling of a general multicopter plant, and safety requirements cover the failsafe measures dealing with the presented safety issues. Based on these requirements, several multicopter modes and events are defined. On this basis, the multicopter plant and control specifications are modelled by automata. Then, a supervisor is synthesized by using SCT. In addition, the authors present three examples to demonstrate the potential conflicting phenomena due to the inappropriate design of control specifications. Finally, based on the obtained supervisor, an implementation method suitable for multicopters is presented, in which the supervisor is transformed into decision‐making codes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".