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Record W2131420771 · doi:10.1109/robot.2002.1013596

Modal force based input shaper for vibration suppression of flexible payloads

2003· article· en· W2131420771 on OpenAlexaff
Tong Zhou, A.A. Goldenberg, Jean W. Zu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPayload (computing)Input shapingControl theory (sociology)Feed forwardModalVibrationNonlinear systemVibration controlTrajectoryComputer scienceReduction (mathematics)RobotControl engineeringEngineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Input shaping is a simple but effective method for vibration reduction of linear flexible systems. When systems exhibit nonlinear dynamics, the performance of an input shaper may be degraded. In this paper, a new shaper design scheme is developed for a nonlinear system which consists of a rigid robotic manipulator and a flexible sheet metal payload being grasped at several points by the robot gripper. The new scheme utilizes a parallel shaper structure based on the modal forces of payload vibration. A feedforward/feedback control law is also presented to drive the system to follow its shaped trajectory and to stabilize it at its desired position. Simulation results of a rest-to-rest robot motion control illustrate improved performance of the proposed input shaper, and verify the effectiveness of the control strategy.

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 score0.977
Threshold uncertainty score0.237

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 head0.009
GPT teacher head0.208
Teacher spread0.200 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

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