基于双环自适应滑模控制的高速列车运行追踪  

High-speed Train Operation Tracking Based on Dual Closed-loop Adaptive Sliding Mode Control

作　　者：冯庆胜[1] 薛祥希 姜增鹏 FENG Qingsheng;XUE Xiangxi;JIANG Zengpeng(School of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学自动化与电气工程学院,大连116028

出　　处：《自动化与仪表》2024年第3期26-32,共7页Automation & Instrumentation

基　　金：2019年辽宁省自然科学基金项目(2019-ZD-0094)。

摘　　要：针对列车高速运行跟踪控制问题,提出了一种基于双环结构的RBF神经网络自适应滑模控制算法。首先系统结构分为位移与速度控制子系统,防止列车因单个控制器故障而失控;在此基础上采用积分滑模控制,加强控制器的鲁棒性;同时在速度子系统中引入参数自适应算法与RBF神经网络自适应算法削弱列车受到基本阻力、附加阻力以及不确定性阻力带来的影响;最后通过Lyapunov稳定性分析证明系统的稳定性。通过仿真实验结果表明,位移误差在[-3.3×10^(-4),1.9×10^(-4)]范围以内,速度误差在[-2.1×10^(-2),3.1×10^(-2)]范围以内,该算法可以实现对列车的速度与位移精确追踪。An adaptive sliding mode control algorithm of RBF neural network based on double loop structure is proposed to solve the problem of high-speed train tracking control.Firstly,the system structure is divided into displacement and speed control subsystems to prevent the train from losing control due to the failure of a single controller.On this basis,integral sliding mode control is used to strengthen the robustness of the controller.At the same time,parameter adaptive algorithm and RBF neural network adaptive algorithm are introduced into the speed subsystem to reduce the impact of basic resistance,additional resistance and uncertain resistance.Finally,the stability of the system is proved by Lyapunov stability analysis.Through simulation experiments,the results show that the displacement error is within the[-3.3×10^(-4),1.9×10^(-4)]range and the speed error is within the[-2.1×10^(-2),3.1×10^(-2)]range,and the algorithm can accurately track the speed and displacement of the train.

关 键 词：高速列车 积分滑模 双闭环控制系统 自适应控制 RBF神经网络 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]