MétaCan
Menu
Back to cohort
Record W4386049656 · doi:10.3390/drones7080541

Disturbance Observer-Enhanced Adaptive Fault-Tolerant Control of a Quadrotor UAV against Actuator Faults and Disturbances

2023· article· en· W4386049656 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

VenueDrones · 2023
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)ActuatorDisturbance (geology)Observer (physics)Computer scienceTrajectoryControl engineeringFault toleranceTracking (education)EngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

For a quadrotor unmanned aerial vehicle (UAV), this paper proposes an adaptive sliding mode control (SMC) strategy enhanced with a disturbance observer to attain precise trajectory and attitude tracking performance while compensating for the detrimental impacts of actuator faults and disturbances. First, an adaptive SMC strategy that utilizes an integral sliding surface is presented to enhance the fault-tolerance capabilities of the studied quadrotor UAV against actuator faults. In addition, a disturbance observer is further created to compensate for the disturbances. By integrating the proposed adaptive SMC strategy with the designed disturbance observer, both actuator faults and disturbances can be effectively accommodated. It was theoretically demonstrated that the system is stable while using the proposed adaptive fault-tolerant control strategy. The effectiveness and benefits of the proposed strategy is verified with comparative simulation results under different faulty scenarios.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.013
GPT teacher head0.216
Teacher spread0.202 · 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