基于自适应终端滑模的高速列车迭代学习速度控制  

Adaptive Terminal Sliding Mode Based Iterative Learning Speed Control for High⁃speed Trains

作　　者：张鑫[1] 祝子钧 陈凯生 ZHANG Xin;ZHU Zijun;CHEN Kaisheng(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070

出　　处：《铁道学报》2024年第9期76-84,共9页Journal of the China Railway Society

基　　金：甘肃省自然科学基金(20JR5RA419);甘肃省高等学校创新基金(2022A-045);兰州交通大学-天津大学联合创新基金(2019053)。

摘　　要：针对高速列车的速度追踪控制问题,充分利用列车运行的重复性,考虑工程应用中迭代初始状态不同和复杂的外部环境,提出一种基于线性扩张状态观测器(LESO)的自适应非奇异终端滑模迭代学习控制算法,使系统在任意迭代初值时均能保证追踪精度。提出一种时变非奇异终端滑模面以抑制初态误差影响,采用LESO估计并补偿列车扰动,设计自适应迭代更新律估计LESO的观测误差,设计全饱和自适应迭代控制律计算输入并将其约束于允许范围内。建立类Lyapunov的复合能量函数,通过严格的数学分析证明其迭代域的差分负定性和有界性,证明所设计的时变滑模面可实现渐进收敛,并证明追踪误差在滑模面内可在有限时间内收敛至平衡点。将本文提出的算法与滑模控制、变增益迭代学习控制、自抗扰控制等算法进行比较。仿真结果表明:无论是否存在迭代初始误差,在相同的条件下,本文提出的算法较其他算法具有更强的抗干扰能力,速度追踪精度提高90%及以上,停车误差可迭代收敛至001 m。In order to solve the speed tracking problem of high-speed trains,taking full use of the repetitive character of train operation,and considering the different initial values of iterations and the complex external environment in engineer-ing applications,an adaptive non-singular terminal sliding mode iterative learning control algorithm was proposed based on linear extended state observer(LESO)to guarantee the tracking accuracy of the system at any iterative initial value.First-ly,a time-varying non-singular terminal sliding surface was proposed to suppress the influence of initial errors.LESO was used to estimate and compensate for train resistance and external disturbances,with an adaptive iterative update law de-signed to estimate the observation error of LESO.A fully saturated adaptive iterative learning law was designed to calculate the input and constrain it within the prescribed region.Secondly,with a Lyapunov-like composite energy function estab-lished,the differential negativity and boundedness of the function in the iterative domain was demonstrated through rigor-ous mathematical analysis,thereby proving that the designed time-varying sliding surface can achieve asymptotic conver-gence,and that the tracking error can converge to zero within a finite time in the sliding surface.Finally,the proposed algorithm in this paper was compared with sliding mode control,variable gain iterative learning control,and active dis-turbance rejection control algorithms.The simulation results show that,regardless of the presence of an iterative initial error,the proposed algorithm has better anti-disturbance capability than other algorithms under the same conditions,with 90%and more improvement in speed tracking accuracy and with stopping errors iteratively converging to 001 m.

关 键 词：高速列车 列车自动驾驶 自适应迭代学习控制 扩张状态观测器 初值问题 

分 类 号：U284.48[交通运输工程—交通信息工程及控制]