汽车工程 ›› 2022, Vol. 44 ›› Issue (5): 747-755.doi: 10.19562/j.chinasae.qcgc.2022.05.012

所属专题: 底盘&动力学&整车性能专题2022年

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电液线控制动系统压力反步控制算法研究

石琴,刘鑫,应贺烈,王铭伟,贺泽佳,贺林()   

  1. 1.合肥工业大学汽车与交通工程学院,合肥  230009
    2.合肥工业大学汽车智能与电动实验室,合肥  230009
  • 收稿日期:2021-08-17 修回日期:2021-11-27 出版日期:2022-05-25 发布日期:2022-05-27
  • 通讯作者: 贺林 E-mail:helin@hfut.edu.cn
  • 基金资助:
    安徽省高校协同创新项目(GXXT-2020-076);安徽省发改委新能源与智能网联汽车创新工程项目和江苏省重点研发计划项目(BE2021006-2)

Study on the Backstepping Control Algorithm for the Hydraulic Pressure in Electro-hydraulic Brake-by-wire System

Qin Shi,Xin Liu,Helie Ying,Mingwei Wang,Zejia He,Lin He()   

  1. 1.School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei  230009
    2.Laboratory of Automotive Intelligence and Electrification,Hefei University of Technology,Hefei  230009
  • Received:2021-08-17 Revised:2021-11-27 Online:2022-05-25 Published:2022-05-27
  • Contact: Lin He E-mail:helin@hfut.edu.cn

摘要:

为实现线控制动系统液压精确控制,本文中设计了一种新型线控制动系统,通过对该系统进行动力学分析,建立了面向控制的系统动力学模型,基于该系统模型设计出反步控制算法。利用径向基网络逼近连续函数特性,对与系统状态量相关的非线性摩擦力进行估计,作为反步控制器的补偿,并证明该算法李雅普诺夫稳定。基于电液线控制动系统台架开展了多组制动工况测试,结果表明,所设计的控制策略能实现对线控制动系统液压力的精确控制且反应迅速。

关键词: 线控制动, 液压力控制, 反步控制, RBF神经网络, 李雅普诺夫稳定性

Abstract:

In order to realize the accurate control of hydraulic pressure in brake-by-wire system, a new type of brake-by-wire system is developed in this paper. A control-oriented dynamics model of system is established through system dynamics analysis, and a backstepping control algorithm is designed based on that system model. A radial basis function network is used to approximate the characteristics of continuous function and estimate the non-linear friction, related to system state variables, as the compensation for backstepping controller, with the Lyapunov stability of the algorithm proved. An electro-hydraulic brake-by-wire test bench is built to conduct tests on several braking conditions and the results show that the control strategy designed can achieve the accurate control of hydraulic pressure in brake-by-wire system with fast response.

Key words: brake-by-wire, hydraulic pressure control, backstepping control, RBF neural network, Lyapunov stability