This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessArticle
Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification
by
Yun Zhang
Yun Zhang
,
Mingchen Luan
Mingchen Luan
Zhenyu Tang
Zhenyu Tang
Haitao Yan
Haitao Yan
Long Wang
Long Wang
1
School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China
2
Key Laboratory of Rail Transit Safety Technology and Equipment, Shandong Province Transportation Industry, Jinan 250357, China
3
Degree Programs in Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8573, Ibaraki, Japan
*
Author to whom correspondence should be addressed.
Machines 2025, 13(10), 917; https://doi.org/10.3390/machines13100917 (registering DOI)
Submission received: 28 August 2025
/
Revised: 25 September 2025
/
Accepted: 2 October 2025
/
Published: 5 October 2025
Abstract
To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes.
Share and Cite
MDPI and ACS Style
Zhang, Y.; Luan, M.; Tang, Z.; Yan, H.; Wang, L.
Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification. Machines 2025, 13, 917.
https://doi.org/10.3390/machines13100917
AMA Style
Zhang Y, Luan M, Tang Z, Yan H, Wang L.
Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification. Machines. 2025; 13(10):917.
https://doi.org/10.3390/machines13100917
Chicago/Turabian Style
Zhang, Yun, Mingchen Luan, Zhenyu Tang, Haitao Yan, and Long Wang.
2025. "Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification" Machines 13, no. 10: 917.
https://doi.org/10.3390/machines13100917
APA Style
Zhang, Y., Luan, M., Tang, Z., Yan, H., & Wang, L.
(2025). Improved Deadbeat Predictive Current Predictive Control Based on Low-Complexity State Feedback Controllers and Online Parameter Identification. Machines, 13(10), 917.
https://doi.org/10.3390/machines13100917
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.
