Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results
Abstract
This paper focuses on current control in a permanent-magnet synchronous motor (PMSM). This paper has two main objectives: the first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with the conventional proportional-integral-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by: 1) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and 2) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation.
Recommended Citation
S. Li et al., "Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results," IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3218 - 3230, Institute of Electrical and Electronics Engineers (IEEE), Jul 2020.
The definitive version is available at https://doi.org/10.1109/TCYB.2019.2897653
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Approximate Dynamic Programming (ADP); Neural Network (NN); Permanent-Magnet Synchronous Motor (PMSM); Vector Control; Voltage Source Inverter (VSI)
International Standard Serial Number (ISSN)
2168-2267; 2168-2275
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers Inc., All rights reserved.
Publication Date
01 Jul 2020
PubMed ID
30802881
Comments
This work was supported in part by the U.S. National Science Foundation under Grant IIP 1650564, in part by the Mary K. Finley Endowment, in part by the Missouri S&T Intelligent Systems Center, and in part by the Army Research Laboratory under Cooperative Agreement W911NF-18-2-0260.