MétaCan
Menu
Back to cohort

Discrete-time Observers for a Mechatronics System with PID Controllers Tuned Using SMA

2023· article· en· W4386323903 on OpenAlexaff
Alexandra-Iulia Szedlak-Stinean, Radu‐Emil Precup, Raul‐Cristian Roman, Emil M. Petriu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMechatronicsControl theory (sociology)PID controllerControl engineeringNonlinear systemPosition (finance)Control systemController (irrigation)Discrete time and continuous timeComputer scienceEngineeringDigital controlControl (management)Electronic engineeringMathematicsArtificial intelligenceTemperature control

Abstract

fetched live from OpenAlex

This paper proposes a conventional control structure with four discrete-time observers for estimating the angular position for an electromechanical plant with rigid body and flexible drive dynamics. The described estimation techniques are used to estimate states in a complex and nonlinear mechanism, namely ECP Model 220 Industrial Plant Emulator (ECPM220IPE), which has the capability to emulate, design and implement several industrial applications. The conventional control structure employs, in conjunction with all these estimation techniques, a Proportional-Integral-Derivative (PID) controller with parameters optimally tuned using a metaheuristic Slime Mould Algorithm (SMA) that solves the optimization problems with objective functions described as the sums of squared control errors multiplied by time. The control system performance is proved and validated through real-time experimental and digital simulation results focusing on position control and to highlight how the specified control system performance was obtained a comparative analysis of the four estimation techniques with the optimally tuned parameters is also presented.

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: Methods · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.721

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Control Systems OptimizationFrench-language works237,207