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Single Loop Control of a Common DC-Bus-Configured Traction Motor Emulator Using State Feedback Linearization Method

2021· article· en· W4213261477 on OpenAlexaff
Arvind H. Kadam, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsControl theory (sociology)Inner loopEngineeringCurrent loopComputer scienceVoltageController (irrigation)Electrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Nowadays, the LCL (inductor-capacitor-inductor) filter is an integral part of every power electronic converter system operating on current control. Though higher order, the LCL filter due to its high harmonic suppression capability are widely adopted in back-to-back converter systems or grid connected converter systems. Normally, with LCL filter, the control system follows double closed loop with outer loop controlling DC voltage and inner loop controlling AC current. However, for some special applications like common DC-bus-configured motor emulator (ME) system, the DC bus voltage is not required to be controlled. In addition, the presence of LCL filter introduces cross coupling of flux and torque component in case of AC motor emulation. The double closed loop control is generally adopted to decouple torque and flux components, however the controller design is more complex process. Therefore, this paper presents a single loop control of a common DC-bus-configured ME system with LCL filter based on state feedback linearization method. The decoupling equations in direct and quadrature axis are derived here. The simulation results presented for a 2.0 kW PMSM motor validate the stability as well as closed loop operation of the system with single loop control.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.695

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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 teacher head, 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

Citations4
Published2021
Admission routes1
Has abstractyes

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