MétaCan
Menu
Back to cohort
Record W2785536324 · doi:10.1137/16m1066683

Multiobjective $\mathcal{H}_2$/$\mathcal{H}_\infty$ Control Design Subject to Multiplicative Input Dependent Noises

2018· article· en· W2785536324 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSIAM Journal on Control and Optimization · 2018
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsnot available
FundersNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMultiplicative functionMathematicsMultiplicative noiseDiagonalBounded functionNash equilibriumOptimal controlAlgebraic numberMatrix (chemical analysis)Algebraic Riccati equationRiccati equationControl (management)Applied mathematicsDiscrete mathematicsControl theory (sociology)Mathematical optimizationMathematical analysisComputer scienceDifferential equation

Abstract

fetched live from OpenAlex

This paper addresses the mixed $\mathcal{H}_2$/$\mathcal{H}_\infty$ control problem for linear discrete-time systems with structured multiplicative input noises. The structured random noises are modelled by a diagonal matrix where individual multiplicative noise is allowed to be different. The Nash game methodology is adopted to deal with such a multiobjective control problem, and a necessary and sufficient condition is analytically given in terms of two cross-coupled modified algebraic Riccati equations (MAREs). Based on the stabilizing solutions to MAREs, bounded causal equilibrium strategies are thus conducted and the resulting control law optimizes some specific performance together with a guaranteed worst case performance. Some relevant issues that can be regarded as special cases of the problem under consideration are also discussed. Moreover, a numerical example is included to show the validity of the present results.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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