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Record W4288489082 · doi:10.1002/acs.3467

Adaptive finite‐time fault‐tolerant control for flexible‐joint robotic stochastic systems with input saturation and sensor fault

2022· article· en· W4288489082 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.

Bibliographic record

VenueInternational Journal of Adaptive Control and Signal Processing · 2022
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsCarleton University
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsControl theory (sociology)BacksteppingFault toleranceComputer scienceNonlinear systemConvergence (economics)Fuzzy logicTracking errorController (irrigation)Control engineeringEngineeringAdaptive controlControl (management)Artificial intelligenceDistributed computing

Abstract

fetched live from OpenAlex

Summary A new fault‐tolerant approach to input saturation and sensor faults is presented for flexible‐joint robotic stochastic nonlinear systems with finite‐time convergence performance. More precisely, a new command filter is designed and embedded into the backstepping framework, which greatly reduces the amount of calculation. By using the estimation ability of fuzzy logic system and less adjustable parameters, the problems of sensor faults and unknown functions are solved, which facilitates the design of controllers. A smooth function and mean‐value theorem are used to deal with the difficulty associated with system signals. A novel simpler controller is developed to ensure that the trajectory tracking error converges to a sufficiently small neighborhood around the origin within finite time, while there are random noises. Finally, the effectiveness of the presented scheme is verified by simulation results.

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)0.001
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 score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.223
Teacher spread0.208 · 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