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Record W2096799782 · doi:10.1115/1.2807181

Tracking Control of Hydraulic Actuators Using a LuGre Friction Model Compensation

2008· article· en· W2096799782 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

VenueJournal of Dynamic Systems Measurement and Control · 2008
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsControl theory (sociology)Hydraulic cylinderActuatorController (irrigation)Compensation (psychology)Hydraulic machineryNonlinear systemControl engineeringElectro-hydraulic actuatorObserver (physics)Parametric statisticsAccelerationHydraulic motorDisplacement (psychology)EngineeringComputer scienceControl (management)MathematicsMechanical engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper addresses the tracking control of hydraulic actuators commonly used in many hydraulically actuated robotic systems. Dynamic model of the entire actuator incorporating highly nonlinear hydraulic functions and the LuGre dynamic friction model is used to arrive at a suitable controller. The controller is augmented with adaptation laws to compensate for parametric uncertainties in the actuator dynamics, hydraulic functions as well as friction with nonuniform force variations. Furthermore, an adaptive observer is used in the controller to avoid the use of acceleration measurement. Therefore, only measurements of displacement, velocity, and hydraulic line pressures are required for the implementation of the proposed controller. Stability and convergence of the control system are theoretically studied. Experimental results are presented verifying the effectiveness of the developed controller.

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 score0.578
Threshold uncertainty score0.679

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 science0.0000.000
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 head0.033
GPT teacher head0.213
Teacher spread0.180 · 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