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Record W2610837471 · doi:10.1049/iet-csr.2019.0039

Failsafe mechanism design of multicopters based on supervisory control theory

2020· preprint· en· W2610837471 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Cyber-Systems and Robotics · 2020
Typepreprint
Languageen
FieldComputer Science
TopicPetri Nets in System Modeling
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSupervisorSupervisory controlCorrectnessSupervisory control theoryController (irrigation)Process (computing)Control (management)Computer scienceMechanism (biology)Event (particle physics)Control engineeringFunction (biology)Fail-safeControl theory (sociology)EngineeringReliability engineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.245
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it