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Record W4388919005 · doi:10.1016/j.ifacol.2023.10.1293

Controller Design for Game Theoretic Steady-State Control: An LMI Approach

2023· article· en· W4388919005 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

VenueIFAC-PapersOnLine · 2023
Typearticle
Languageen
FieldEngineering
TopicExtremum Seeking Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)DiagonalNash equilibriumController (irrigation)Set (abstract data type)Constant (computer programming)Stability (learning theory)Computer scienceState (computer science)Exponential stabilityArgument (complex analysis)Mathematical optimizationMathematicsControl (management)Nonlinear systemAlgorithm

Abstract

fetched live from OpenAlex

We consider a set of LTI agents subject to constant external disturbances seeking to minimize coupled cost functions in steady-state, modelled as a game-theoretic problem. Using a novel framework, the Nash equilibrium seeking problem has been shown to reduce to the design of a set of decentralized stabilizing controllers. Herein, we consider two such controller designs, based off LMI approaches. The first employs a diagonal stability argument, and the second relies on H∞ design coupled with the small gain theorem. Applications to sensor networks are provided and the trade-offs between the two methods 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.240
Teacher spread0.214 · 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