Weighting Factor Elimination Techniques for Model Predictive Control of AC Machines: A Comparison

Direct model predictive control of electrical drives has gained significant acceptance within the research community over the past decade. To address the challenge of weighting factor (WF) tuning, one of the primary issues, remarkable efforts have been made. This paper compares several effective WF elimination methods, commonly referred to as decision-making (DM) methods, in the context of model predictive torque control for an induction motor. Each technique is assessed from multiple perspectives, including control performance, design complexity, and computational demand. Finally, the paper provides a comprehensive analysis of the effectiveness of DM methods.

Authors

Institutions

Publication Details

Published
2025-05-20
DOI
https://doi.org/10.1109/cpe-powereng63314.2025.11027268
Citations
1
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Weighting Factor Elimination Techniques for Model Predictive Control of AC Machines: A Comparison

Recep Yıldız, Patrick Wheeler, Emrah Zerdali, J. Rodríguez et al.
1 citations
Fault Detection and Control Systems
1.21
article

Weighting Factor Elimination Techniques for Model Predictive Control of AC Machines: A Comparison

Recep Yıldız, Patrick Wheeler, Emrah Zerdali, J. Rodríguez, Marco Rivera
article en
1 citations

Abstract

Direct model predictive control of electrical drives has gained significant acceptance within the research community over the past decade. To address the challenge of weighting factor (WF) tuning, one of the primary issues, remarkable efforts have been made. This paper compares several effective WF elimination methods, commonly referred to as decision-making (DM) methods, in the context of model predictive torque control for an induction motor. Each technique is assessed from multiple perspectives, including control performance, design complexity, and computational demand. Finally, the paper provides a comprehensive analysis of the effectiveness of DM methods.

Niğde Ömer Halisdemir Üniversitesi (TR), Ege University (TR)
British Council, Research and Development
Openalex Percentile: Top 20%
Fault Detection and Control Systems
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.