摘要
无线网络时延的存在是制约高速列车无线通信网络发展的关键问题。通过搭建无线网络控制半实物试验平台获取时延样本,针对无线时延序列复杂度高的特点,提出互补集合经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)与奇异值分解(Singular Value Decomposition,SVD)相结合的处理方法,并引入样本熵降低运算量,最终通过混沌粒子群算法优化最小二乘支持向量机(Least Square Support Vector Machine,LSSVM)模型,完成时延预测;以高速列车牵引制动模块为被控对象,设计基于多幂次趋近律的滑模控制器进行时延补偿,采用混沌粒子群算法对控制器参数进行智能寻优,将ITAE指标(Integrated Time and Absolute Error)改进为ITAE*作为适应度函数,以减小跟踪误差,提高响应速度。结果表明:经CEEMD分解后最大分量样本熵由原来的2.7以上降至2.0以下,采用SVD对最大样本熵的本征模函数(Intrinsic Mode Function1,IMF1)分量进行二次处理后,其样本熵降至1.5以下,有效降低了预测难度;改进后的时延预测均方误差(Mean Square Error,MSE)、平均绝对误差(Mean Absolute Error,MAE)和平均绝对百分误差(Mean Absolute Percentage Error,MAPE)分别降至0.139 6,0.296 4和0.008 3,预测精度有所提高;对于较长时延,进行时延补偿控制后其ITAE*指标仅为补偿前的0.136%,速度跟踪累计绝对误差为补偿前的0.696%,高速列车运行状态改变时速度跟踪曲线无明显抖振现象,同时当高速列车遇到扰动时速度跟踪曲线可快速回归平稳状态。研究成果可为高速列车引入无线网络控制提供一种新的方法。
The existence of wireless network time delay is the key problem restricting the development of wireless communication network for high-speed train.Time delay samples are obtained by building a semiphysical network control test platform based on wireless communication.Aiming at the high complexity of time delay,a processing method combining complementary ensemble empirical mode decomposition(CEEMD)and singular value decomposition(SVD)is proposed.Sample entropy is introduced to reduce the amount of computation.Finally,LSSVM model optimized by chaotic particle swarm optimization is used to complete time delay prediction.Taking traction-brake module of high-speed train as the controlled object,a sliding mode controller based on multi-power reaching law is designed for time delay compensation.Chaotic particle swarm optimization algorithm is used to optimize the controller parameters intelligently.Integrated time and absolute error(ITAE)are improved into ITAE*for the fitness function to reduce the tracking error and improve the response speed.The results show that the sample entropy of the maximum component is reduced from‘above 2.7’to‘below 2.0’using CEEMD decomposition.After secondary treatment with SVD,the sample entropy of Intrinsic Mode Function 1(IMF1)component with rest of the maximum sample entropy reduced to 1.5,which effectively reduces the difficulty of prediction.After improvement,Mean Square Error(MSE),Mean Absolute Error(MAE)and Mean Absolute Percentage Error(MAPE)of delay prediction are reduced to 0.1396,0.2964 and 0.0083 respectively,and the prediction accuracy is improved.For longer time delay,ITAE*index is only 0.136%of that before compensation,and the cumulative absolute error of speed tracking is 0.696%of that before compensation.The speed and racking curve have no obvious vibration when the running state of the high-speed train changes and can quickly return to the stable state when the train encounters disturbance.The research results can provide a new approach to introducing wireless netw
作者
刘洋
窦顺坤
张丽艳
LIU Yang;DOU Shunkun;ZHANG Liyan(School of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian Liaoning 116028,China;School of Computer and Communication Engineering,Dalian Jiaotong University,Dalian Liaoning 116028,China)
出处
《中国铁道科学》
EI
CAS
CSCD
北大核心
2023年第6期162-171,共10页
China Railway Science
基金
辽宁省教育厅科学研究项目(JDL2020020)。
关键词
高速列车无线通信网络
互补集合经验模态分解
粒子群算法
滑模控制
时延补偿
Wireless communication network for high-speed train
Complementary ensemble empirical mode decomposition
Particle swarm optimization algorithm
Sliding mode control
Time delay compensation
