Cascade-free predictive adhesion control for IPMSM-driven electric trains

被引:0
|
作者
Ren, Jiao [1 ]
Li, Ruiqi [2 ]
机构
[1] Urban Vocat Coll Sichuan, Chengdu 610110, Peoples R China
[2] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Peoples R China
关键词
adhesion control; cascade-free; maximize longitudinal acceleration force; perturbation and observation; predictive speed control; CONTROL-SYSTEM; OPERATION; BRAKING;
D O I
10.24425/bpasts.2024.151375
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The application of active adhesion control to the traction control system of an electric train holds great appeal for maximizing longitudinal acceleration force. Most of the currently reported works regulate the adhesion between wheel and rail by adjusting the torque reference of a cascade motor drive controller, which suffers from slow speed response and excessive start torque. This article proposes a cascade- free predictive adhesion control strategy for electric trains powered by an interior permanent magnet synchronous motor (IPMSM) to address these issues. The proposed control scheme utilizes an improved perturbation and observation method to predict the time-varying wheel-rail adhesion state and determine the optimal slip speed. The initial setpoint reference command from the driver master is then adjusted to a dynamic reference that continuously adapts to the predicted adhesion conditions. Finally, the predictive speed control method is employed to ensure rapid convergence of the tracking objective. The simulation and hardware-in-the-loop testing results confirm that this approach achieves excellent dynamic performance, particularly during the train start-up phase and in the high-speed weak magnetic area of the IPMSM.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Quantized Data Driven Model-Free Adaptive Predictive Control for a Class of Nonlinear Systems
    Liu, Genfeng
    Hou, Zhongsheng
    2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 1105 - 1110
  • [42] Data-driven model-free predictive control for microstage with coupling and hysteresis nonlinearities
    Lin, Shiqi
    Chen, Xuesong
    ASIAN JOURNAL OF CONTROL, 2024, 26 (06) : 3040 - 3053
  • [43] Model-Free Predictive Current Control for Three-Level Inverter-Fed IPMSM With an Improved Current Difference Updating Technique
    Yu, Feng
    Zhou, Chenhui
    Liu, Xing
    Zhu, Chenguang
    IEEE TRANSACTIONS ON ENERGY CONVERSION, 2021, 36 (04) : 3334 - 3343
  • [44] Integrated Control for Path Tracking and Stability Based on the Model Predictive Control for Four-Wheel Independently Driven Electric Vehicles
    Xie, Yunfeng
    Li, Cong
    Jing, Hui
    An, Weibiao
    Qin, Junji
    MACHINES, 2022, 10 (10)
  • [45] A Communication-Free and Model-Free Predictive Control for a Dynamic IPT System With High Power Factor for Electric Vehicles
    Kalat, Sina Navaiyan
    Vaez-Zadeh, Sadegh
    Zakerian, Ali
    Babaki, Amir
    Ebel, Thomas
    IEEE ACCESS, 2023, 11 : 96773 - 96783
  • [46] Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles
    Sun, Jing
    Xing, Guojing
    Zhang, Chenghui
    ENERGIES, 2017, 10 (04):
  • [47] Data-driven model predictive control design for offset-free tracking of nonlinear systems
    Park, Byungjun
    Kim, Jong Woo
    Lee, Jong Min
    INTERNATIONAL JOURNAL OF CONTROL, 2023, 96 (06) : 1408 - 1423
  • [48] Vibration Control Method for an Electric City Bus Driven by a Dual Motor Coaxial Series Drive System Based on Model Predictive Control
    Wang, Wenwei
    Li, Yiding
    Shi, Junhui
    Lin, Cheng
    IEEE ACCESS, 2018, 6 : 41188 - 41200
  • [49] Anti-disturbance control of oxygen feeding for vehicular fuel cell driven by feedback linearization model predictive control-based cascade scheme
    Chen, Jinzhou
    Li, Jianwei
    Xu, Zhezhuang
    Wang, Ya-Xiong
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2020, 45 (58) : 33925 - 33938
  • [50] An improved data-driven predictive optimal control approach for designing hybrid electric vehicle energy management strategies
    Yin, Cheng
    Zeng, Xiangrui
    Yin, Zhouping
    APPLIED ENERGY, 2024, 375