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Record W2112624509 · doi:10.1109/acc.2009.5160456

GPC-based remote control for hydraulic position control systems in a networked environment

2009· article· en· W2112624509 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsModel predictive controlControl theory (sociology)Controller (irrigation)Position (finance)ActuatorComputer scienceNetworked control systemControl systemMarkov chainNode (physics)Stability (learning theory)Control engineeringControl (management)EngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper is concerned with the design of the remote hydraulic position control system (HPCS) using the modified generalized predictive control (M-GPC) method. Both sensor-to-controller (S-C) and controller-to-actuator (C-A) network-induced delays are modeled as Markov chains. The M-GPC uses the available output and previously predicted control information at the controller node to compute the future control sequences. Different from the conventional generalized predictive control (GPC) in which only the first element in control sequences is used, the M-GPC employs the whole control sequences to compensate for the network-induced delays in both S-C and C-A links. The closed-loop system is further formulated as a special jump linear system. The sufficient and necessary conditions for the stochastic stability are derived. The experimental tests for an HPCS are given to verify the effectiveness of the proposed method.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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

Quick stats

Citations3
Published2009
Admission routes1
Has abstractyes

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