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Record W2008133970 · doi:10.1177/0954410013505951

Active vibration control of flexible manipulator using auto disturbance rejection and input shaping

2013· article· en· W2008133970 on OpenAlex
Bo Luo, Hai Huang, Jinjun Shan, Hidekazu Nishimura

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

VenueProceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsYork University
FundersMinistry of Education of the People's Republic of ChinaYork University
KeywordsControl theory (sociology)Disturbance (geology)VibrationCompensation (psychology)Controller (irrigation)ActuatorInput shapingVibration controlManipulator (device)Control engineeringComputer scienceActive vibration controlEngineeringControl (management)RobotArtificial intelligenceAcoustics

Abstract

fetched live from OpenAlex

This paper presents a vibration control strategy for a flexible manipulator with a collocated piezoelectric sensor/actuator pair. A hybrid vibration controller is proposed by combining the input shaping technique with auto disturbance rejection controller. The parameters of the closed-loop system can be adjusted to the known values by disturbance compensation and linear feedback using the auto disturbance rejection controller. This way, input shaper can be designed without accurate parameters of the flexible manipulator. Both simulation and experiments are conducted to validate the proposed control algorithm. The results verified the effectiveness of the proposed controller in vibration suppression of flexible manipulator.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.194
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