何静,刘光伟,张昌凡,孙健,程翔.重载机车粘着性能参数的极大似然辨识方法[J].电子测量与仪器学报,2017,31(2):170-177
重载机车粘着性能参数的极大似然辨识方法
Maximum likelihood identification method for adhesion performance parameters of heavy duty locomotive
  
DOI:10.13382/j.jemi.2017.02.002
中文关键词:  重载机车  极大似然  系统辨识  二次规划  时变遗忘因子
英文关键词:heavy duty locomotive  maximum likelihood estimation  system identification  quadratic programming  time varying forgetting factor
基金项目:国家自然科学基金(61273157,61473117)、 湖南省自然科学基金(2016JJ5007)资助项目
作者单位
何静 1. 湖南工业大学电气与信息工程学院株洲412007;2. 电传动控制与智能装备湖南省重点实验室株洲412007 
刘光伟 湖南工业大学电气与信息工程学院株洲412007 
张昌凡 湖南工业大学电气与信息工程学院株洲412007 
孙健 湖南工业大学电气与信息工程学院株洲412007 
程翔 湖南工业大学电气与信息工程学院株洲412007 
AuthorInstitution
He Jing 1. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China; 2. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hunan Province, Zhuzhou 412007, China 
Liu Guangwei College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China 
Zhang Changfan College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China 
Sun Jian College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China 
Cheng Xiang College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China 
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中文摘要:
      针对重载机车运行中机车的粘着利用率低、易空转、易打滑的问题,提出一种对轨面粘着性能参数的实时在线估计算法。首先从分析机车粘着行为出发,选用Kiencke的粘着 蠕滑模型作为辨识模型,然后算法利用极大似然意义下的模型参数辨识框架,将参数估计转化为二次规划问题求解,进而构造出辨识的迭代算法。同时考虑到轮轨环境突变的不可测,辨识算法引入时变遗忘因子来适应轨面环境的切换。仿真结果表明,该算法能及时跟踪上轮轨环境的变化,有效辨识出粘着性能参数。
英文摘要:
      A real time online estimation algorithm on the adhesion performance parameters of the rail surface is presented for the problems such as low adhesion utilization, easy idling and easy slipping of the locomotive in the operation of heavy duty locomotive. Firstly, based on the analysis of locomotive adhesion behavior, Kiencke adhesion creep model is selected as the identification model. Then, the algorithm uses model parameter identification framework under the significance of the maximum likelihood to transform the parameter estimation into solving quadratic programming problem, and the iterative algorithm for identification is constructed. At the same time, considering that the rail environment mutation cannot be measured, the time varying forgetting factor is introduced into the identification algorithm to adapt to the switching of rail surface environment. The simulation results show that the algorithm is able to track the change of wheel rail environment timely, and identify the parameters of adhesion performance effectively.
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