Data-driven Model-free Adaptive Control Method for High-speed Electric Multiple Unit

被引：0
|
作者：
Li Z.-Q. [1 ,2 ]
Zhou L. [1 ,2 ]
Yang H. [1 ,2 ]
机构：
[1] School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang
[2] State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Automatic train operation; data-driven; energy saving control; model-free adaptive control; partial format data model; velocity tracking;
D O I：
10.16383/j.aas.c211068
中图分类号：
学科分类号：
摘要：
For the speed tracking control problem of electric multiple unit, the dependence of the existing model-based control methods on the system dynamic model and the complexity of the time-varying parameter estimation algorithm of the traditional model-free adaptive control are both considered. The improved multiple-input multiple-output (MIMO) partial format dynamic linearization-improved model-free adaptive control (PFDL-iMFAC) method is introduced into the automatic train operation system. On the basis of model-free adaptive control, this control method considers the sliding time window, increases the adjustable degree of freedom and design flexibility, and adds the penalty term to the energy function in the input criterion function to reduce the energy loss. It provides a compromise method for the tracking accuracy and energy-saving operation of electric multiple unit, and realizes energy-saving operation under the premise of satisfying the good speed tracking effect of electric multiple unit. Finally, CRH380A electric multiple unit is taken as the object for simulation experiment. Compared with the traditional model-free adaptive control, the speed tracking error of each power unit in the proposed control algorithm is within ±0.2 km/h, and the acceleration one is within ±0.65 m/s2 and the change is stable, saving 9.86% of energy compared with the traditional model-free adaptive control method. © 2023 Science Press. All rights reserved.
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收藏
页码：437 / 447
页数：10
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