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Record W3112093917 · doi:10.37628/ijra.v5i2.909

Link position control of flexible joint robots based on Sliding Mode Technique

2019· article· en· W3112093917 on OpenAlexvenueno aff
Genliang Xiong, Jingxin Shi, Bo Xu

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2019
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)RobotSliding mode controlControl engineeringJoint stiffnessBandwidth (computing)Computer scienceRoboticsNonlinear systemRobustness (evolution)EngineeringStiffnessArtificial intelligenceControl (management)TelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The inherent highly nonlinear coupling and system uncertainties make the controller design for a flexible-joint robot extremely difficult. The goal of the control of any robotic system is to achieve high bandwidth, high accuracy of trajectory tracking and high robustness, whereby the high bandwidth for flexible-joint robot is the most challenging issue. This paper is dedicated to design such a link position controller with high bandwidth based on sliding mode technique. Then, two control approaches (1) Extended Regular-form approach, 2) The cascaded control structure based on the sliding mode estimator approach) are presented for the link position tracking control of flexible-joint robot, considering the dynamics of AC-motors in robot joints, and compared with the singular perturbation approach. These two link position controllers are tested and verified by the simulation studies with different reference trajectories and under different joint stiffness. Cite this Article: Xiong Genliang, Jing-xin shi, Bo-xu. Link Position Control of Flexible Joint Robots Based on Sliding Mode Technique. International Journal of Robotics and Automation. 2019; 5(2): 1–22p.

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 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 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 score0.955
Threshold uncertainty score0.281

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

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