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Record W4317106110 · doi:10.18280/jesa.550607

Control of Double Link Flexible Robotic Manipulator System

2022· article· en· W4317106110 on OpenAlexvenueno aff
Mohamad Iqhmannabil Karim, Azrul Azim Abdullah Hashim, Nor Maniha Abdul Ghani

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2022
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsnot available
Fundersnot available
KeywordsMobile manipulatorPID controllerControl engineeringController (irrigation)RoboticsRobotActuatorManipulator (device)Control systemComputer scienceParallel manipulatorEngineeringSimulationArtificial intelligenceMobile robotTemperature control

Abstract

fetched live from OpenAlex

Flexible manipulator is widely used in the implementation of industial robotic due to its advantages such as low weight, low power consumption, higher load capacity, high-speed operation, small actuators and low production costs. However, the position and speed of flexible manipulator system are very difficult to control due to the tip vibration that result in degradation of performance. Modelling and control of a double-link flexible robot manipulator are presented in this study. Controlling the movement of a double-link manipulator, on the other hand, has proven to be a challenging task, especially when a flexible framework is used. Moreover, most double-link flexible manipulator system models are not developed based on real hardware. Hence, this project aims to develop a Solidworks design for double link flexible robotics manipulator (DLFRM) as well as a real hardware prototype. The control position performance of DLFRM was analyzed, and the controllers were tested on a hardware prototype. This project started with a simulation of both controllers, which are PID and FLC. The simulation was designed in Solidworks and exported to Simulink and then converted as Simscape. Then, the hardware for each controller was validated using the control parameter in the simulation. The joints for the robot manipulator were designed in Solidwork and built using 3D printing.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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