Data-Driven Lyapunov-Based Model Predictive Control for Improved Trajectory Tracking in Multi-Wheel-Independent-Drive Electric Vehicle
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
IEEE transactions on vehicular technology
ISSN (print)
0018-9545
ISSN (electronic)
1939-9359
ISSN (linking)
0018-9545
Publisher
IEEE
Publication forum ID
57585
Publication forum level
3
Publication appears in FT-list
No
SNIP-level of the publication
2.07
Country of publication
United States
Internationality
Yes
Detailed publication information
Publication year
2025
Reporting year
2025
Journal/series issue number
Date of Publication: 24 July 2025
DOI
10.1109/TVT.2025.3587541
Language of publication
English
Co-publication information
International co-publication
Yes
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
213 Electronic, automation and communications engineering, electronics
Keywords
Multi-wheel vehicle; Deep learning; Data-driven modeling; Lyapunov-based MPC; Trajectory tracking; Vehicle dynamics; Predictive models; Accuracy; Tires; Trajectory tracking; Residual neural networks; Neural networks; Wheels; Predictive control; Motors
Funding information
Funding information in the publication
This work was supported in part by Key R&D Program of Shandong Province, China under Grant 2023CXGC010111.
Source database ID
WoS ID
WOS:001642388000021
Scopus ID
2-s2.0-105012284275