MétaCan
Menu
Back to cohort
Record W2099406474 · doi:10.1109/ccece.2008.4564511

Real-time performance of a nonlinear controller based IM drive

2008· article· en· W2099406474 on OpenAlex
Mohammad Nasir Uddin, Sang Woo Nam

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsRockwell Automation (Canada)Lakehead University
Fundersnot available
KeywordsBacksteppingControl theory (sociology)Controller (irrigation)Nonlinear systemComputer scienceControl engineeringDigital signal processorInduction motorAdaptive controlRotor (electric)Electronic speed controlDigital signal processingEngineeringControl (management)Artificial intelligenceComputer hardware

Abstract

fetched live from OpenAlex

This paper presents the real-time performance evaluation of a nonlinear controller for speed control of an induction motor (IM) drive. Neglecting the iron loss in an induction motor model causes performance deterioration. In this work, an adaptive backstepping based nonlinear controller incorporating the iron loss is developed under the parameter uncertainties. The adaptive backstepping technique is utilized to estimate the parameters online and maintain the global stability of the drive. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1104 for a laboratory 1/3 hp IM. Experimental results show that the proposed controller achieves rotor speed tracking objectives successfully and improves dynamic responses as compared to the one without parameter adaptation.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
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
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.167
Teacher spread0.158 · 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