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Record W2110140328 · doi:10.1109/robot.1996.503788

Adaptive control for constrained robots without using regressor

2002· article· en· W2110140328 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)TrajectoryRobotConstraint (computer-aided design)Controller (irrigation)Nonlinear systemAdaptive controlComputer scienceDegrees of freedom (physics and chemistry)Control (management)Robot end effectorTracking (education)Robot controlControl engineeringMathematical optimizationMathematicsMobile robotArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

This paper addresses the problem of controller design for constrained robots with the consideration of computational efficiency. An adaptive control algorithm, based on the reduced dynamics formulation, is presented for trajectory tracking of an end-effector on a constrained surface with specified constraint forces. It is shown that the control objective can be achieved without exact knowledge of robot dynamics and online calculation of nonlinear dynamic functions. The control algorithm is constructed with at most four control parameters to be adjusted adaptively, and that number is determined independently of the number of degrees of freedom of the robotic manipulators.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.598

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)0.0010.000

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 head0.070
GPT teacher head0.254
Teacher spread0.184 · 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

Quick stats

Citations11
Published2002
Admission routes2
Has abstractyes

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