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Record W4415943724 · doi:10.1016/j.ifacol.2025.10.152

A Cascade Control Approach for Motion Systems Driven By Nonlinear Reluctance Actuator

2025· article· en· W4415943724 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIFAC-PapersOnLine · 2025
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of GuelphMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsControl theory (sociology)Robustness (evolution)CascadeActuatorNonlinear systemMagnetic reluctancePID controllerControl systemPosition (finance)

Abstract

fetched live from OpenAlex

This paper introduces a cascade control strategy designed to improve tracking performance in motion systems driven by a reluctance actuator (RA). The proposed approach features an inner current control loop that compensates for the RA’s nonlinear behavior across varying operating conditions. An outer position control loop, incorporating a PID controller augmented by an extended high-gain observer (EHGO), is employed to accurately track the reference position while addressing unknown system dynamics. The effectiveness of the current control design is validated through both simulation and experimental studies. Results confirm that the proposed controller successfully linearizes the RA response across different load conditions and air gaps, producing a dynamic behavior comparable to that of a spring-mass-damper system. Furthermore, the position control significantly enhances reference tracking accuracy and minimizes tracking errors. Simulations also demonstrate the controller’s robustness against system uncertainties and measurement noise.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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