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Record W2100185852 · doi:10.1109/tcst.2009.2030789

Adaptive Regulation in Switched Bimodal Systems: An Experimental Evaluation

2009· article· en· W2100185852 on OpenAlexaff
Zhizheng Wu, Foued Ben Amara

Bibliographic record

VenueIEEE Transactions on Control Systems Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRegulatorControl theory (sociology)NotationAdaptive controlSurface (topology)Adaptive systemBeam (structure)Computer scienceMathematicsControl engineeringEngineeringControl (management)Artificial intelligenceGeometry

Abstract

fetched live from OpenAlex

This paper presents experimental results on the adaptive exact output regulation in a switched bimodal mechanical system subject to unknown sinusoidal exogenous inputs representing reference or disturbance signals. The adaptive regulator design exploits the$Q$parameterization of regulators for the switched system, and where the$Q$parameter is tuned online to yield the desired regulator. The proposed adaptive regulator is evaluated on an experimental setup motivated by the flying height regulation problem in hard disk drives. In the experimental setup, the tip of a flexible beam is supposed to maintain a constant separation with respect to a surface with an unknown profile, while also being subject to an unknown disturbance force. The system exhibits a switching behaviour depending on whether contact takes place between the surface to be tracked and the tip of the beam. The experimental results successfully demonstrate the effectiveness of the proposed approach in achieving exact output regulation against unknown sinusoidal exogenous inputs and in the presence of switching in the system dynamics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations10
Published2009
Admission routes1
Has abstractyes

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