MétaCan
Menu
Back to cohort
Record W4292712335 · doi:10.1080/00207179.2022.2112088

Tracking control of soft dielectric elastomer actuator based on nonlinear PID controller

2022· article· en· W4292712335 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
TopicDielectric materials and actuators
Canadian institutionsConcordia University
FundersHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsPID controllerControl theory (sociology)Nonlinear systemElastomerActuatorTracking (education)DielectricControl engineeringController (irrigation)Dielectric elastomersMaterials scienceComputer scienceControl (management)EngineeringPhysicsComposite materialArtificial intelligenceTemperature controlPsychologyElectrical engineering

Abstract

fetched live from OpenAlex

Soft dielectric elastomer actuators (SDEAs) have manifested tremendous potentials in the field of soft robots. However, the tracking control of the SDEA still confronts enormous challenges because of its complicated nonlinear characteristics. In this paper, a nonlinear proportional-integral-derivative (NPID) controller is devised to fulfil the tracking control of the SDEA. A dynamic model of the SDEA is established, and then applied to construct a simulation system to coarsely tune the parameters of the NPID controller based on the iterative optimisation algorithm. Subsequently, according to the results of the coarse tuning, the parameters of the NPID controller are finely tuned in practical experiments. Finally, the NPID controller of the SDEA is proved to be effective via several tracking control experiments with different reference trajectories.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.004
GPT teacher head0.207
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