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Record W4400726721 · doi:10.1109/tcst.2024.3422048

Fault-Tolerant Cooperative Control Design for Car-Like Vehicles Subject to Actuator Faults and Fading Channels

2024· article· en· W4400726721 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

VenueIEEE Transactions on Control Systems Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFadingActuatorComputer scienceFault toleranceSubject (documents)Channel (broadcasting)EngineeringAutomotive engineeringComputer networkElectrical engineeringDistributed computing

Abstract

fetched live from OpenAlex

This brief addresses the problem of fault-tolerant cooperative control (FTCC) for a group of car-like vehicles experiencing actuator faults. The main feature of this study is the transmission of vehicle’s state information via fading channels. It is challenging to compensate for actuator faults and maintain vehicle’s stability in the presence of unreliable communication links. To cope with such fault conditions, this work introduces an integral terminal sliding mode control developed by means of received faded neighborhood state information. The fading channel’s effect and the nonlinearity of vehicle dynamics are carefully analyzed by providing rigorous proofs with the Lyapunov stability theorem. In this study, the settling time function relies on design parameters rather than the initial states, which is essential for real applications. The effectiveness of the proposed controller is validated in a real system using the latest Quanser self-driving car (QCar) platform.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.230
Teacher spread0.219 · 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