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Record W2003007041 · doi:10.1115/imece2014-36052

A Robust Controller of Multi DOF-Cooperating Planar Robotic Manipulators Using a Tuned PID Approach

2014· article· en· W2003007041 on OpenAlexaff
Dhafar Al-Ani, Hamed Afshari, Saeid Habibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPID controllerControl theory (sociology)Controller (irrigation)MATLABControl engineeringComputer scienceRoboticsRobotEngineeringArtificial intelligenceControl (management)Temperature control

Abstract

fetched live from OpenAlex

Usually, a dynamic system with impact conditions is an interesting problem with practical applications in the fields of dynamics, vibrations, and control. One difficulty in controlling robotics (i.e., a multi DOF two-cooperating or two-link planar) is the subject to impact between the end-effectors of manipulators is that the dynamics (i.e., equations of motion) are different when the system status changes suddenly from a non-contact state to a contact state. In this paper, a Tuned PID controller with different design scenarios is developed to regulate the states of two dynamic systems that collide. Further, in this work, three types of errors are used to compare among different cases that are; (1) the steady state error, (2) the root mean square error, and (3) the final value error. The results of the Tuned PID controller are compared to those obtained by a classical PID controller. The PID controller is tuned using the Ziegler–Nicholas approach. The simulation results of the robotic manipulators confirmed the theoretical effectiveness of the proposed controller, based on MATLAB/Simulink. Unlike the classical PID results (i.e., the impact-induced force is found to be 2.0 N), the Tuned PID controller successfully determined the impact-induced force as same as the desired force (i.e., 0.6 N). Moreover, the Tuned PID satisfied all other desired design values.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.644

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 head0.032
GPT teacher head0.203
Teacher spread0.171 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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