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Record W3135283790 · doi:10.1109/tcyb.2021.3054373

Adaptive Control and Application for Nonlinear Systems With Input Nonlinearities and Unknown Virtual Control Coefficients

2021· article· en· W3135283790 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 Cybernetics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaKey Technology Research and Development Program of ShandongTaishan Scholar Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsControl theory (sociology)Nonlinear systemAdaptive controlController (irrigation)Dead zoneStability (learning theory)Computer scienceControl systemTracking (education)Control (management)Control engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This article is devoted to an adaptive tracking control problem for nonlinear systems with input deadzone and saturation, whose virtual control coefficients include the known and unknown terms. A novel smooth function is first introduced to approximate the input nonlinearities. By utilizing an auxiliary variable and the Nussbaum gain technique, an improved real control signal is constructed to handle the uncertainties of the virtual control coefficients and input nonlinearities. Furthermore, an adaptive tracking controller is constructed and applied to the attitude control of a quadrotor, which guarantees the boundedness of all the signals in the resulting closed-loop system. Finally, both stability analysis and simulation results validate the effectiveness of the developed control strategy.

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 score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.207
Teacher spread0.198 · 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