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Record W4293193175 · doi:10.1080/00207179.2022.2117083

Robust adaptive fixed-time control for a class of nonlinear systems with actuator faults

2022· article· en· W4293193175 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 Control · 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)Nonlinear systemActuatorClass (philosophy)Adaptive controlMathematicsComputer scienceControl engineeringControl (management)EngineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper addresses the output tracking problem of adaptive fixed-time fault-tolerant control for a class of nonlinear systems. In particular, we focus on two types of actuator faults: lock-in-place and loss of effectiveness. Specifically, a robust adaptive fault-tolerant controller is designed to compensate for the effect of actuator faults. By applying the classical backstepping design algorithm and fixed-time control theory, an adaptive fixed-time controller is developed, ensuring that the tracking error converges into a small neighbourhood around the origin within a fixed time where the convergence time is independent of initial conditions. Theoretical analysis and simulation outcomes prove that the closed-loop system is fixed-time stable and all signals are bounded by choosing the parameters appropriately. The effectiveness and feasibility of the presented control scheme are verified through the 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 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 score0.910
Threshold uncertainty score0.829

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 communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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