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Record W1907781290 · doi:10.1109/asspcc.2000.882514

Design of a multivariable self-tuning PID controller with an internal model structure

2002· article· en· W1907781290 on OpenAlex

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPID controllerMultivariable calculusControl theory (sociology)Internal modelProcess (computing)Computer scienceProcess controlControl systemControl engineeringSystem identificationControl (management)Temperature controlEngineeringData modelingArtificial intelligence

Abstract

fetched live from OpenAlex

PID control schemes have been widely used for various process control systems for a long time. However, since such processes have relatively large time-delays and uncertainties caused by modeling errors and system fluctuation, it is non-trivial to choose suitable PID parameters. In the paper, a design scheme of a multivariable self-tuning PID controllers is proposed for unknown processes with time-delays. A parallel internal model is firstly designed in order to compensate the time-delays. The PID control system is then constructed for the augmented system. The PID control parameters are tuned adaptively based on online process identification and the relationship between the PID control and the minimum variance control laws.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score0.422

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 head0.010
GPT teacher head0.185
Teacher spread0.175 · 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

Quick stats

Citations5
Published2002
Admission routes1
Has abstractyes

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