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Record W2109470906 · doi:10.1109/3477.979968

Robust damping control of mobile manipulators

2002· article· en· W2109470906 on OpenAlexaff
Sheng Lin, A.A. Goldenberg

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2002
Typearticle
Languageen
FieldEngineering
TopicControl and Dynamics of Mobile Robots
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Mobile manipulatorKinematicsController (irrigation)Robust controlControl engineeringBounded functionComputer scienceMotion controlStability (learning theory)Mobile robotManipulator (device)Control (management)Control systemEngineeringRobotMathematicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

A novel robust control technique, robust damping control (RDC), is introduced. An RDC controller is further developed for the motion control of a mobile manipulator subject to kinematic constraints. The knowledge of dynamic parameters of the mobile manipulator is assumed to be completely unknown. The proposed RDC controller is capable of disturbance-rejection in the presence of unknown bounded disturbance, without requiring the knowledge of its bound. The stability of the closed-loop system is guaranteed. The controller has a simple structure and can be easily implemented in applications. Experimental tests on a 2-DOF robotic manipulator illustrate that the proposed control is significantly better than conventional robust control.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.190
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2002
Admission routes1
Has abstractyes

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