TY - GEN
T1 - A Novel Neural-Networks-Enhanced Linear Active Disturbance Rejection Current Control Strategy for Variable-Flux Machine
AU - Wang, Mingqiao
AU - Dong, Xinyu
AU - Wu, Yunlong
AU - Liu, Yong
AU - Zheng, Ping
AU - Sui, Yi
AU - Jia, Boru
N1 - Publisher Copyright:
© 2025 Korean Institute of Electrical Engineers Electrical Machinery and Energy Conversion Systems Society.
PY - 2025
Y1 - 2025
N2 - This paper proposes a novel neural-networkenhanced linear active disturbance rejection (LADR) current control strategy for variable-flux machines (VFMs) to address the challenge of significant inductance parameter variations during magnetization state (MS) regulation. The developed solution integrates a backpropagation neural network (BPNN) with the LADR control strategy, where the BPNN dynamically predicts the inductance parameters of the machine using real-time MS and d-axis current, enabling adaptive parameter adjustment of the LADR controller. Implemented on variable-magnetic-circuit series-parallel VFM (VMC-SPVFM), comparative simulations are established to demonstrate that the proposed strategy achieves superior operational performance, particularly in dynamic current tracking under varying MS conditions, compared with conventional proportional-integral (PI) control.
AB - This paper proposes a novel neural-networkenhanced linear active disturbance rejection (LADR) current control strategy for variable-flux machines (VFMs) to address the challenge of significant inductance parameter variations during magnetization state (MS) regulation. The developed solution integrates a backpropagation neural network (BPNN) with the LADR control strategy, where the BPNN dynamically predicts the inductance parameters of the machine using real-time MS and d-axis current, enabling adaptive parameter adjustment of the LADR controller. Implemented on variable-magnetic-circuit series-parallel VFM (VMC-SPVFM), comparative simulations are established to demonstrate that the proposed strategy achieves superior operational performance, particularly in dynamic current tracking under varying MS conditions, compared with conventional proportional-integral (PI) control.
KW - Variable-flux machine (VFM)
KW - current control
KW - linear active disturbance rejection control (LADRC)
KW - magnetization state (MS) regulation
KW - neural network
UR - https://www.scopus.com/pages/publications/105032876666
U2 - 10.23919/ICEMS66262.2025.11317449
DO - 10.23919/ICEMS66262.2025.11317449
M3 - Conference contribution
AN - SCOPUS:105032876666
T3 - ICEMS 2025 - 28th International Conference on Electrical Machines and Systems
SP - 2104
EP - 2109
BT - ICEMS 2025 - 28th International Conference on Electrical Machines and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Conference on Electrical Machines and Systems, ICEMS 2025
Y2 - 16 November 2025 through 19 November 2025
ER -