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Adaptive Backstepping Sliding Mode Based Fault-Tolerant Cooperative Control for Multiple UAVs under Thrust Loss Faults and Input Saturation

2023· article· en· W4385259899 on OpenAlexafffund
Zhongyu Yang, Ziquan Yu, Guili Xu, Youmin Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
FundersFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsBacksteppingControl theory (sociology)ThrustLyapunov stabilityConvergence (economics)Lyapunov functionComputer scienceSliding mode controlSaturation (graph theory)Fault toleranceAdaptive controlFault (geology)Control engineeringEngineeringControl (management)MathematicsNonlinear systemPhysicsAerospace engineeringGeologyArtificial intelligence

Abstract

fetched live from OpenAlex

To overcome input saturation and loss of thrust effectiveness problems for multiple fixed-wing unmanned aerial vehicles (UAVs), a fault-tolerant cooperative control (FTCC) based on adaptive backstepping sliding mode control (BSMC) method is developed. An auxiliary dynamic system is constructed to solve the input saturation problem. Furthermore, adaptive laws are proposed to estimate the thrust effectiveness and lumped unknown term. Stability of the system and finite-time convergence of the error signals are proved by Lyapunov analysis. Finally, the effectiveness of the proposed control scheme is verified by the simulations.

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)0.000
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.027
GPT teacher head0.256
Teacher spread0.229 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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