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Record W2103868579 · doi:10.1115/1.4007550

Output Regulation in Bimodal Systems With Application to Surface Tracking in the Presence of Contact Vibrations

2012· article· en· W2103868579 on OpenAlexafffund
Zhizheng Wu, Foued Ben Amara

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2012
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsControl theory (sociology)Parameterized complexityRegulatorConstraint (computer-aided design)Observer (physics)VibrationComputer scienceControl engineeringSet (abstract data type)Linear systemMathematicsEngineeringControl (management)AlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

Motivated by a class of surface tracking problems in mechanical systems subject to contact vibrations, this paper considers a regulation problem for discrete-time switched bimodal linear systems where it is desired to achieve output regulation against exogenous input signals featuring known deterministic and unknown random components. A first step in the regulator design involves constructing a set of observer-based parameterized stabilizing controllers that satisfy a sufficient regulation condition for the switched system against the known deterministic disturbance or reference signals. In the second step, an additional performance constraint is added to identify, from among the already constructed regulators, those that provide the best regulation performance against the unknown random disturbances. A corresponding regulator synthesis algorithm is developed based on iteratively solving properly formulated bilinear matrix inequalities. The proposed regulator is successfully evaluated on an experimental setup involving a switched bimodal mechanical system subject to contact vibrations, hence demonstrating the effectiveness of the proposed regulation 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.

How this classification was reachedexpand

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)0.003
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: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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 teacher head, 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

Citations1
Published2012
Admission routes2
Has abstractyes

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