摘要
针对车-车通信过程中因信道容量约束产生的列车控制精度降低的问题,提出基于RBFNN(radical basis function neural network,RBFNN)的自适应量化滑模ATO(automatic train operation,ATO)控制方法.基于自适应量化滑模控制技术,利用RBFNN对列车模型受到的附加阻力及未知扰动进行自适应逼近补偿,并引入基本阻力参数自适应机制以实现列车高精度控制,保证列车运行安全.仿真结果表明:该算法能够保证列车高精度跟踪理想的运行曲线,实现列车在站间的平稳运行和精确停车.
Aiming at the problem of the train control accuracy reduction caused by the channel capacity constraints in the train-to-train communication process,an self-adaptive quantitation sliding mode ATO(automatic train operation)control method based on RBFNN(radial basis function neural network)is proposed.Based on the adaptive quantization sliding mode control technology,RBFNN is used to adaptively approximate and compensate the additional resistance and unknown disturbance to the train model,and the adaptive mechanism of basic resistance parameters is introduced to realize the high-precision control and ensure the safety of the train operation.The simulation results show that the algorithm can ensure the train to track the desired operating curve with high precision,and realize the smooth operation and precise parking of the train between stations.
作者
杨军霞
张友鹏
YANG Jun-xia;ZHANG You-peng(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《兰州交通大学学报》
CAS
2022年第1期68-73,共6页
Journal of Lanzhou Jiaotong University
基金
甘肃省教育厅优秀研究生“创新之星”项目(2021CXZX-552)。
关键词
ATO
车-车通信
RBFNN
自适应控制
量化控制
ATO
train-to-train communication
RBFNN
self-adaptive control
quantitative control
