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Record W2154327602 · doi:10.1109/cdc.2010.5717315

Performance limitations of the servomechanism problem when the number of tracking/disturbance poles increases

2010· article· en· W2154327602 on OpenAlexaff
E.J. Davison, Simon Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsServomechanismControl theory (sociology)Disturbance (geology)Tracking (education)Measure (data warehouse)Controller (irrigation)sortMathematicsRobust controlComputer scienceIndex (typography)Control systemEngineeringControl (management)Control engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we study the cheap control problem and determine what some of the inherent system limitations are in achieving high performance for LTI systems. In particular, we observe that a fundamental difficulty in designing a high performance controller for a system may occur, which is related to the infinite transmission zero structure of the system. A continuous measure, called the Toughness Index, is introduced to characterize such limitations. We then apply these results to the robust servomechanism problem (RSP), and show that the Toughness Index of the RSP becomes worst as the number of tracking/disturbance poles to be tracked/regulated increases. This implies that high performance control in the RSP cannot be obtained for a large number of tracking/disturbance poles, even for minimum phase systems.

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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
Teacher spread0.190 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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