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RING COUPLING-BASED COLLABORATIVE FAULT-TOLERANT CONTROL FOR MULTI-ROBOT ACTUATOR FAULT

2018· article· en· W2901778172 on OpenAlexvenueno aff
Jing He, Lin Mi, Jianhua Liu, Xiang Cheng, Zhenzhen Lin, Changfan Zhang

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2018
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsnot available
FundersNatural Science Foundation of Hunan ProvinceNational Natural Science Foundation of China
KeywordsActuatorCoupling (piping)Fault (geology)Computer scienceRing (chemistry)Fault toleranceControl theory (sociology)RobotControl (management)Control engineeringEngineeringArtificial intelligenceDistributed computingChemistryGeologyMechanical engineering

Abstract

fetched live from OpenAlex

In this paper, a ring coupling-based fault-tolerant control scheme is proposed to synchronize multi-robot systems with actuator faults.The control scheme includes a sliding-mode control law and a slidingmode observer.The sliding-mode control law is given via the ring coupling strategy for a multi-robot system.To observe the status of unmeasurable variables and unknown fault control information, a sliding mode observer is designed such that the unknown fault information is accurately reconstructed by using the equivalent principle of sliding-mode variable structure.Based on the observed status value and fault reconstructed value, the sliding-mode control law can be adjusted online and thus the collaborative fault-tolerant control is realized online.Finally, simulations and experiments are given to demonstrate the effectiveness of the proposed method.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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