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Record W2970865877 · doi:10.1515/cppm-2017-0016

Real Time Modeling and Control of Three Tank Hybrid System

2017· article· en· W2970865877 on OpenAlexaff
K. Sathishkumar, V. Kirubakaran, T. K. Radhakrishnan

Bibliographic record

VenueChemical Product and Process Modeling · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsControl theory (sociology)ReachabilityLinear-quadratic regulatorController (irrigation)Control engineeringMode (computer interface)Process (computing)Range (aeronautics)EngineeringComputer scienceControl (management)AlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This study discusses the modeling and linear quadratic regulator (LQR) controller based closed loop control of a three tank hybrid (TTH) process. A pseudo random binary signal (PRBS) based excitation data obtained from a real time TTH setup is utilized in validating its first principle model (FPM). Based on top and bottom interactions, various modes prevalent are considered based on steady state physical reachability analysis of the TTH for a given input range for controller design. The FPM is linearized using nominal values of process parameters using Jacobians from each existing mode. LQR controllers are designed for each mode. A supervisory structure is designed for selecting estimation model and controller for each appropriate mode. Results from real time servo tracking and disturbance rejection experiments are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.216
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2017
Admission routes1
Has abstractyes

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