Prescribed Performance Active Braking Control with Reference Adaptation for High-Speed Trains

被引:3
|
作者
Zhang, Rui [1 ]
Peng, Jun [2 ]
Chen, Bin [3 ]
Gao, Kai [3 ]
Yang, Yingze [2 ]
Huang, Zhiwu [1 ]
机构
[1] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
[2] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China
[3] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha 410114, Peoples R China
基金
中国国家自然科学基金; 湖南省自然科学基金;
关键词
active braking control; feedback linearization; prescribed performance; optimal slip ratio; train adhesion; FEEDBACK LINEARIZATION; SYSTEMS; STATE;
D O I
10.3390/act10120313
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Active braking control systems are vital for the safety of high-speed trains by leading the train operation at its maximum adhesion state. The train adhesion is a nonlinear function of the slip ratio and varies with the uncertain wheel-rail contact conditions. A nonlinear active braking control with rapid and accurate tracking performance is highly required for train braking systems. This paper proposes a novel prescribed performance active braking control with reference adaptation to obtain the maximum adhesion force. The developed feedback linearization controller employs a prescribed performance function that specifies the convergence rate, steady-state error, and maximum overshoot to ensure the transient and steady-state control performance. Furthermore, in the designed control approach, a continuous-time unscented Kalman filter is introduced to estimate the uncertainty of wheel-rail adhesion. The estimation is utilized to represent uncertainty and compensate for the prescribed performance control law. Finally, based on the estimated wheel-rail adhesion, an on-line optimal slip ratio generation algorithm is proposed for the adaptation of the reference wheel slip. The stability of the system is provided, and experiment results validate the effectiveness of the proposed method.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Adaptive slip ratio estimation for active braking control of high-speed trains
    Chen, Bin
    Huang, Zhiwu
    Zhang, Rui
    Jiang, Fu
    Liu, Weirong
    Li, Heng
    Wang, Jing
    Peng, Jun
    ISA TRANSACTIONS, 2021, 112 : 302 - 314
  • [2] Dynamic Performance of the High-Speed Trains under Braking Failure
    Hu, Yanlin
    Ge, Xin
    Ling, Liang
    Wang, Kaiyun
    ICRT 2021: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON RAIL TRANSPORTATION, 2022, : 120 - 125
  • [3] High-speed trains subject to abrupt braking
    Minh Thi Tran
    Ang, Kok Keng
    Van Hai Luong
    Dai, Jian
    VEHICLE SYSTEM DYNAMICS, 2016, 54 (12) : 1715 - 1735
  • [4] Adaptive braking control for high-speed trains with input time delays
    Tan, Chang
    Li, Yiqing
    Journal of Railway Science and Engineering, 2022, 19 (04) : 1071 - 1080
  • [5] Prescribed performance tracking control for adjacent virtual coupling high-speed trains with input saturation
    Huang, Deqing
    Jia, Yuqi
    Li, Xuefang
    Zhu, Lei
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2023, 33 (15) : 9450 - 9468
  • [6] Braking Dynamics of High-speed Passenger Trains.
    Hendrichs, Wolfgang
    eb - Elektrische Bahnen, 1988, 86 (06): : 200 - 207
  • [7] Computationally Inexpensive Tracking Control of High-Speed Trains With Traction/Braking Saturation
    Song, Qi
    Song, Yong-duan
    Tang, Tao
    Ning, Bin
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2011, 12 (04) : 1116 - 1125
  • [8] Braking process identification of high-speed trains for automatic train stop control
    Liu, Xiaoyu
    Xun, Jing
    Ning, Bin
    Wang, Cheng
    ISA TRANSACTIONS, 2021, 111 : 171 - 179
  • [9] Traction/Braking Control of High Speed Trains Based Active Antiskid Constraints
    Cai, Wenchuan
    Zhu, Dan
    Lu, Lingyu
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 1619 - 1624
  • [10] An optimization control strategy for braking system of high-speed trains under partial loss of braking force
    Wang, Jiuhe
    Chen, Zhiyong
    Chen, Zhiwen
    Peng, Tao
    Peng, Lijuan
    Liu, Ruifeng
    2022 IEEE 17TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION, ICCA, 2022, : 109 - 114