Enhanced Dynamic Control for Flux-Switching Permanent Magnet Machines Using Integrated Model Predictive Current Control and Sliding Mode Control

Enhancing the dynamic response of Flux-Switching Permanent Magnet Synchronous Machines (FSPMSMs) is crucial for high-performance applications such as electric vehicles, renewable energy systems, and industrial automation. Conventional Proportional Integral (PI) controllers within model predictive current control (MPCC) frameworks often struggle to meet the demands of rapid transient response and precise speed tracking, particularly under dynamic operating conditions. To address these challenges, this paper presents a hybrid control strategy that integrates Sliding Mode Control (SMC) into the speed loop of MPCC, aiming to significantly improve the dynamic response and control robustness of FSPMSMs. The feasibility and effectiveness of the proposed approach are validated through high-fidelity real-time simulations using OPAL-RT Technologies’ OP5707XG simulator. Two control schemes are compared: MPCC with a PI controller in the speed loop (MPCC-PI) and MPCC with SMC in the speed loop (MPCC-SMC). Testing was conducted under various operating scenarios, including starting tests, load variations, speed ramping, and speed reversals. The results demonstrate that the MPCC-SMC strategy achieves superior dynamic performance, faster settling times, smoother transitions, and enhanced steady-state precision compared to the MPCC-PI scheme. The comparative results confirm that the MPCC-SMC method outperforms conventional MPCC strategies, making it a compelling solution for advanced motor drive applications requiring enhanced dynamic control.

Authors

Institutions

Publication Details

Journal
Energies
Published
2025-02-21
DOI
https://doi.org/10.3390/en18051061
Citations
3
Primary Topic
Electric Motor Design and Analysis
Type
article
Field-Weighted Citation Impact
2.19
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhanced Dynamic Control for Flux-Switching Permanent Magnet Machines Using Integrated Model Predictive Current Control and Sliding Mode Control

Mohsin Jamil, Mohammadreza Mamashli
3 citations
Energies
Electric Motor Design and Analysis
2.19
article

Enhanced Dynamic Control for Flux-Switching Permanent Magnet Machines Using Integrated Model Predictive Current Control and Sliding Mode Control

Mohsin Jamil, Mohammadreza Mamashli
article en
3 citations

Abstract

Enhancing the dynamic response of Flux-Switching Permanent Magnet Synchronous Machines (FSPMSMs) is crucial for high-performance applications such as electric vehicles, renewable energy systems, and industrial automation. Conventional Proportional Integral (PI) controllers within model predictive current control (MPCC) frameworks often struggle to meet the demands of rapid transient response and precise speed tracking, particularly under dynamic operating conditions. To address these challenges, this paper presents a hybrid control strategy that integrates Sliding Mode Control (SMC) into the speed loop of MPCC, aiming to significantly improve the dynamic response and control robustness of FSPMSMs. The feasibility and effectiveness of the proposed approach are validated through high-fidelity real-time simulations using OPAL-RT Technologies’ OP5707XG simulator. Two control schemes are compared: MPCC with a PI controller in the speed loop (MPCC-PI) and MPCC with SMC in the speed loop (MPCC-SMC). Testing was conducted under various operating scenarios, including starting tests, load variations, speed ramping, and speed reversals. The results demonstrate that the MPCC-SMC strategy achieves superior dynamic performance, faster settling times, smoother transitions, and enhanced steady-state precision compared to the MPCC-PI scheme. The comparative results confirm that the MPCC-SMC method outperforms conventional MPCC strategies, making it a compelling solution for advanced motor drive applications requiring enhanced dynamic control.

EnergiesVol. 18(5)
Memorial University of Newfoundland (CA), Université Bourgogne Franche-Comté (FR)
Openalex Percentile: Top 13%
Electric Motor Design and Analysis
2.19
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.