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基于高速列车控制系统分数阶微分算子方程的故障诊断动态非线性输入预测研究。

Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system.

机构信息

National Engineering Research Center of Rail Traffic Control System, Beijing Jiaotong University, Beijing 100044, China.

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

出版信息

Chaos. 2019 Jan;29(1):013130. doi: 10.1063/1.5085397.

Abstract

In order to control the nonlinear high-speed train with high robustness, the fractional order control of nonlinear switching systems is studied. The fractional order controller is designed for a class of nonlinear switching systems by the fractional order backstepping method. In this paper, a simple and effective online updating scheme of model coefficients is proposed by using the flexibility of the model predictive control algorithm and its wide range of model accommodation. A stochastic discrete nonlinear state space model describing the mechanical behavior of a single particle in a high-speed train is constructed, and the maximum likelihood estimation of the parameters of a high-speed train is transformed into an optimization problem with great expectations. Finally, numerical comparison experiments of motion characters of two high-speed trains are given. The results show the effectiveness of the proposed identification method.

摘要

为了实现具有高鲁棒性的非线性高速列车控制,研究了非线性开关系统的分数阶控制。采用分数阶反推法设计了一类非线性开关系统的分数阶控制器。本文通过模型预测控制算法的灵活性及其广泛的模型适应性,提出了一种简单有效的模型系数在线更新方案。构建了一个描述高速列车中单个粒子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估计转化为具有大期望的优化问题。最后,给出了两种高速列车运动特性的数值比较实验。结果表明了所提出的辨识方法的有效性。

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