Composite Speed Control of Linear Induction Machine Based on Model Predictive Flux Control with Sliding Mode Disturbance Observer for Linear Metro

A novel sliding mode control (SMC) based model predictive flux control (MPFC), integrated with a sliding mode disturbance observer (SMDO), is proposed to enhance the speed control performance of a linear induction machine (LIM) under disturbances. Firstly, a finite set model predictive flux control (FS-MPFC) is established to mitigate the drawbacks associated with the traditional model predictive thrust control (MPTC), primarily in reducing the tedious tuning of weighting factor. Secondly, a speed control loop is designed to improve the speed transient response and tracking performance, and meanwhile reduce thrust ripple. Thirdly, the SMDO is developed to estimate the overall disturbances of the LIM drive system. Moreover, the estimated disturbance is used for feed-forward compensation approach to increase the disturbance rejection capacity of the whole system. Finally, extensive simulation and experiments have fully demonstrated that the proposed method has fast speed response, improved steady state performance and robustness against load disturbance compared to MPFC-SMC and MPFC-PI.

Authors

Institutions

Publication Details

Published
2025-06-05
DOI
https://doi.org/10.1109/precede63178.2025.11131114
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Composite Speed Control of Linear Induction Machine Based on Model Predictive Flux Control with Sliding Mode Disturbance Observer for Linear Metro

Khalil Ur Rehman, Yirong Tang, Abdul Khalique Junejo, Lin Liang et al.
Advanced Algorithms and Applications
article

Composite Speed Control of Linear Induction Machine Based on Model Predictive Flux Control with Sliding Mode Disturbance Observer for Linear Metro

Khalil Ur Rehman, Yirong Tang, Abdul Khalique Junejo, Lin Liang, Samir A. Hamad, Wei Xu
article en

Abstract

A novel sliding mode control (SMC) based model predictive flux control (MPFC), integrated with a sliding mode disturbance observer (SMDO), is proposed to enhance the speed control performance of a linear induction machine (LIM) under disturbances. Firstly, a finite set model predictive flux control (FS-MPFC) is established to mitigate the drawbacks associated with the traditional model predictive thrust control (MPTC), primarily in reducing the tedious tuning of weighting factor. Secondly, a speed control loop is designed to improve the speed transient response and tracking performance, and meanwhile reduce thrust ripple. Thirdly, the SMDO is developed to estimate the overall disturbances of the LIM drive system. Moreover, the estimated disturbance is used for feed-forward compensation approach to increase the disturbance rejection capacity of the whole system. Finally, extensive simulation and experiments have fully demonstrated that the proposed method has fast speed response, improved steady state performance and robustness against load disturbance compared to MPFC-SMC and MPFC-PI.

Beni-Suef University (EG), Benha University (EG), Institute of Process Engineering (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences
Openalex Percentile: Top 72%
Advanced Algorithms and Applications
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.

Composite Speed Control of Linear Induction Machine Based on Model Predictive Flux Control with Sliding Mode Disturbance Observer for Linear Metro — Khalil Ur Rehman, Yirong Tang, et al. · (2025) | TGRS Research Map | TGRS