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Record W2972370396 · doi:10.23919/acc.2019.8814344

Min-max economic MPC of networked control systems with transmission delays

2019· article· en· W2972370396 on OpenAlex
Yawen Mao, Su Liu, Benjamin Decardi‐Nelson, Jinfeng Liu

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
KeywordsControl theory (sociology)Model predictive controlController (irrigation)ActuatorScheme (mathematics)Computer scienceNonlinear systemProcess (computing)Transmission (telecommunications)Networked control systemSequence (biology)Control systemContinuous stirred-tank reactorControl (management)Process controlOptimal controlControl engineeringEngineeringMathematical optimizationMathematics

Abstract

fetched live from OpenAlex

In this work, we consider a min-max economic model predictive control (EMPC) for nonlinear networked control systems (NCSs) subject to external disturbances and transmission delays in both sensor-to-controller and controller-to-actuator channels. The min-max EMPC scheme incorporates the disturbances within the design of the model predictive controller by optimizing the input for the worst case along the predictions, and thus can achieve better performance than the normal EMPC scheme. The semi-feedback min-max optimization algorithm is used to generate the control sequence to compensate for delayed control input. Simulation results of a numerical example and a CSTR process example are provided to demonstrate the applicability and effectiveness of our approach.

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: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.458

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 head0.002
GPT teacher head0.148
Teacher spread0.147 · 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

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Citations1
Published2019
Admission routes1
Has abstractyes

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