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首页>《中国测试》期刊>本期导读>隧道压力波模拟加载系统遗忘开闭环高阶控制

隧道压力波模拟加载系统遗忘开闭环高阶控制

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作者:李新, 陈春俊, 艾永军, 周建容

作者单位:西南交通大学机械工程学院, 四川 成都 610031


关键词:压力波模拟加载系统;迭代学习控制;开闭环高阶学习律;遗忘因子


摘要:

\t为研究高速列车通过隧道时产生的压力波对车体气密性和车内压力舒适度的影响,建立隧道压力波模拟加载系统。该系统具有非线性、多扰动、多容耦合以及加载的压力波幅值大和变化剧烈等特点,带来控制速度和精度上的难度。为准确模拟加载隧道压力波,采用遗忘开闭环高阶迭代学习控制算法进行控制,利用AMESim和Simulink联合仿真平台进行控制仿真,并对比几种不同学习律的控制效果。仿真结果表明:遗忘开闭环高阶学习律在第7个周期时,压力控制最大误差绝对值已降低到0.358 2 kPa,相对于开环PID和遗忘因子开环PID型学习律的1.23 kPa和0.946 2 kPa,分别减少70.87%和62.14%,该算法可增加系统稳定性,使得隧道压力波的加载更加快速准确。


Tunnel pressure wave simulation loading system forgetting open-closed loop high-order control
LI Xin, CHEN Chunjun, AI Yongjun, ZHOU Jianrong
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Abstract: In order to study the influence of pressure wave produced by high-speed train passing through the tunnel on the airtightness of the car body and the comfort degree of pressure in the car, a tunnel pressure wave simulation loading system was established. The system has the characteristics of nonlinear, multi-disturbance, multi-capacitance coupling, large amplitude and drastic variation of pressure wave, which makes the control speed and precision difficult. In order to accurately simulate the loading tunnel pressure wave, the forgetting open-closed loop high-order iterative learning control algorithm is used for control. The simulation is carried out by AMESim and Simulink joint simulation platform, and the control effects of several different learning laws are compared. The simulation results show that forgetting open-closed loop high-order learning law at the 7th cycle, the maximum absolute value of the pressure control error has been reduced to 0.358 2 kPa. Compared with 1.23 kPa and 0.946 2 kPa of open-loop PID and forgetting factor open-loop PID learning laws, the error is reduced by 70.87% and 62.14% respectively. The stability of the system is increased, and the pressure wave loading of the tunnel is faster and more accurate.
Keywords: pressure wave simulation loading system;iterative learning control;open-closed loop high-order learning law;forgetting factor
2019, 45(1):145-149  收稿日期: 2018-08-25;收到修改稿日期: 2018-09-27
基金项目: 国家自然科学基金项目(51475387)
作者简介: 李新(1994-),男,吉林德惠市人,硕士研究生,专业方向为自动化测试技术
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