Multi-Objective Hierarchical Optimization Design of Compound Electromagnetic Linear Actuator

Conventional electromagnetic linear actuators (ELA) have difficulty in balancing efficiency, response and accuracy, so a novel compound ELA is researched, featuring a coaxial integrated design of moving coil and moving iron actuators that switch between co-drive mode and separate drive mode based on the service environment. In order to further enhance the comprehensive performance of the actuator and improve its adaptability to extreme working conditions, a multi-objective hierarchical optimization strategy was proposed based on the NSGA-II algorithm by analyzing its structural principle, magnetic circuit characteristics and electromagnetic force characteristics. The moving coil actuator, as the main driving component, and the moving iron actuator, as the auxiliary driving component, are selected as the first- and second-layer optimization objects for multi-objective optimization design. Results indicate that the actuator achieves starting forces of 587.6[Formula: see text]N and 191.4[Formula: see text]N under cooperative drive mode and separate drive mode, respectively. Following optimization, these forces increased by approximately 2.2% and 22.4%, while the mover mass was reduced by about 7.2%. The actuator’s thrust density, response speed, and structural compactness were effectively enhanced. The proposed approach can be used to solve the complex multi-objective optimization tasks in ELA designing.

Authors

Institutions

Publication Details

Journal
International Journal of Pattern Recognition and Artificial Intelligence
Published
2026-02-12
DOI
https://doi.org/10.1142/s0218001426560045
Primary Topic
Electric Motor Design and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Objective Hierarchical Optimization Design of Compound Electromagnetic Linear Actuator

Zhang Qing, Xu Shanzhen, Fan Xinyu, Wang Cheng et al.
International Journal of Pattern Recognition and Artificial Intelligence
Electric Motor Design and Analysis
article

Multi-Objective Hierarchical Optimization Design of Compound Electromagnetic Linear Actuator

Zhang Qing, Xu Shanzhen, Fan Xinyu, Wang Cheng, Shen Xin, Hao Kangqi, Fan Aimin, Zhu Jianhui
article en

Abstract

Conventional electromagnetic linear actuators (ELA) have difficulty in balancing efficiency, response and accuracy, so a novel compound ELA is researched, featuring a coaxial integrated design of moving coil and moving iron actuators that switch between co-drive mode and separate drive mode based on the service environment. In order to further enhance the comprehensive performance of the actuator and improve its adaptability to extreme working conditions, a multi-objective hierarchical optimization strategy was proposed based on the NSGA-II algorithm by analyzing its structural principle, magnetic circuit characteristics and electromagnetic force characteristics. The moving coil actuator, as the main driving component, and the moving iron actuator, as the auxiliary driving component, are selected as the first- and second-layer optimization objects for multi-objective optimization design. Results indicate that the actuator achieves starting forces of 587.6[Formula: see text]N and 191.4[Formula: see text]N under cooperative drive mode and separate drive mode, respectively. Following optimization, these forces increased by approximately 2.2% and 22.4%, while the mover mass was reduced by about 7.2%. The actuator’s thrust density, response speed, and structural compactness were effectively enhanced. The proposed approach can be used to solve the complex multi-objective optimization tasks in ELA designing.

International Journal of Pattern Recognition and Artificial Intelligence
Jiangsu University of Science and Technology (CN), Huaiyin Institute of Technology (CN)
Openalex Percentile: Top 57%
Electric Motor Design and Analysis
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.