高速列车非线性模型的极大似然辨识

衷路生; 李兵; 龚锦红; 张永贤; 祝振敏

doi:10.3724/SP.J.1004.2014.02950

高速列车非线性模型的极大似然辨识

doi: 10.3724/SP.J.1004.2014.02950

1.
华东交通大学电气与电子工程学院南昌 330013

基金项目:

国家自然科学基金(61263010,60904049),江西省青年科学基金(20114BAB211014),江西省教育厅研究项目(GJJ14399),国家留学基金(2011836118)资助

详细信息

作者简介:
李兵华东交通大学电气与电子工程学院硕士研究生. 主要研究方向为系统辨识, 统计学习理论. E-mail: libing87@gmail.com

通讯作者:
衷路生华东交通大学电气与电子工程学院副教授. 主要研究方向为系统辨识, 统计学习理论. 本文通信作者.E-mail: lszhongzju@gmail.com

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出版历程
- 收稿日期: 2013-10-10
- 修回日期: 2014-03-19
- 刊出日期: 2014-12-20

Maximum Likelihood Identification of Nonlinear Model for High-speed Train

1.
School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013

Funds:

Supported by National Natural Science Foundation of China (61263010, 60904049), Natural Science Foundation for the Youth of Jiangxi Province (20114BAB211014), Research Project of Jiangxi Educational Department (GJJ14399), and Grant of China Scholarship Council (2011836118)

摘要

摘要: 提出高速列车非线性模型的极大似然(Maximum likelihood, ML)辨识方法,适合于高速列车在非高斯噪声干扰下的非线性模型的参数估计.首先,构建了描述高速列车单质点力学行为的随机离散非线性状态空间模型,并将高速列车参数的极大似然(ML)估计问题转化为期望极大(Expectation maximization, EM)的优化问题; 然后,给出高速列车状态估计的粒子滤波器和粒子平滑器的设计方法,据此构造列车的条件数学期望,并给出最大化该数学期望的梯度搜索方法,进而得到列车参数的辨识算法,分析了算法的收敛速度; 最后,进行了高速列车阻力系数估计的数值对比实验. 结果表明, 所提出的辨识方法的有效性.
- 高速列车 /
- 系统辨识 /
- 极大似然 /
- 平滑滤波器 /
- 梯度搜索
Abstract: A maximum likelihood (ML) system identification method is proposed for parameter estimation of nonlinear dynamic high-speed train model subject to non-gaussian noise. Firstly, a stochastic nonlinear discrete state-space model is established to describe the dynamic behavior of high-speed train as a single-point-mass object. The expectation-maximization (EM) approach is employed to compute the ML parameter estimates. In addition, the techniques of particle filtering and particle smoothing are given to estimate the nonlinear state of high-speed train, which is used to compute approximation of the conditional expectation. Furthermore, gradient-based search method is presented to maximize the conditional expectation. And the identification algorithm is given for parameter estimation of high-speed train. The convergence rate of the identification algorithm is also discussed in detail. Finally, numerical simulation study of parameter estimation for high-speed train is implemented and the results show the effectiveness of the proposed ML identification method.
- High-speed train /
- system identification /
- maximum likelihood (ML) /
- smoothing filters /
- gradient-based search

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参考文献(14)

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