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Record W2149463154 · doi:10.1109/cca.1993.348318

A novel variable structure control scheme for an industrial robot: theory and experiments

2002· article· en· W2149463154 on OpenAlexaff
Yong Cao, Y. Stepanenko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsControl theory (sociology)RobotSliding mode controlController (irrigation)Bounded functionStability (learning theory)HyperplanePID controllerComputer scienceRobot controlSurface (topology)Control engineeringMobile robotMathematicsArtificial intelligenceEngineeringControl (management)Nonlinear systemTemperature control

Abstract

fetched live from OpenAlex

Dynamic behavior of a system in the sliding mode is entirely defined by the sliding surface. Customarily, the surface is selected as a hyperplane in the system state-space resulting in a PD type sliding surface. This is not the only possible structure, and other designs with more complex or time-varying surfaces may provide definite advantages. We present a new design procedure and stability analysis for robotic variable structure controllers with PID like sliding surface. Two versions of the controllers are presented: regular and adaptive. The former is very simple and can operate with an unknown robot dynamic model; the only information required is a bound on one parameter. The latter provides an online estimation for this bound. Both versions of the controllers are robust with respect to bounded external disturbances and some unmodeled dynamic effects. The control schemes are applied to an industrial robot without requiring the knowledge of the dynamic model and the parameter values of the robot. Experiments conducted with a Reis industrial robot have shown stable and accurate performance of the developed controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.593

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 head0.050
GPT teacher head0.246
Teacher spread0.196 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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