欢迎访问《汽车安全与节能学报》，
汽车安全与节能学报

JASE ›› 2018, Vol. 9 ›› Issue (3): 288-294.DOI: 10.3969/j.issn.1674-8484.2018.03.007

• 汽车安全 • 上一篇    下一篇

基于多模型切换的智能汽车横向控制

业红玲1，田 英2，张 杰3， 宋能学3 ，黄 鹤3   

  1. （1. 蚌埠学院，蚌埠 233000，中国；2. 河海大学，南京 210098，中国；3. 合肥工业大学 汽车与交通工程学院，合肥 230009，中国）
  • 收稿日期:2018-04-27 出版日期:2018-09-30 发布日期:2018-10-08
  • 作者简介:业红玲 (1981—)，女( 汉)，安徽，讲师。E-mail: yhl@bbc.edu.cn。
  • 基金资助:

    安徽省自然科学基金(1508085QE92) ；安徽省科技重大专项项目(15CZZ02039) ；安徽高校自然科学研究项目(KJ2016A457)。

Lateral control of intelligent vehicle based on multiple model transition method

YE Hongling1, TIAN Ying2, ZHANG Jie3, SONG Nengxue3, HUANG He3   

  1. (1.Bengbu University, Bengbu 233000, China; 2. Hohai University, Nanjing 210098, China; 3. School of Automobile
    and Traffic Engineering, Hefei University of Technology, Hefei 230009, China)
  • Received:2018-04-27 Online:2018-09-30 Published:2018-10-08

PDF

378

摘要：

        智能汽车面对的道路环境复杂易变，在某些极端工况下汽车侧向动力学进入非线性区域，侧偏刚度发生显著变化。针对智能汽车的轮胎侧偏刚度摄动，在智能汽车横向控制结构基础上，推导横向控制模型。以自适应侧偏刚度作为切换参数，设计具有鲁棒自适应特性的智能汽车横向控制器，该控制器通过侧偏刚度划分为多个局部鲁棒控制器，通过鲁棒控制裕度指标进行全局控制器的切换控制，并进行了硬件在环台架测试试验。结果表明：多模型切换的横向控制方法较常规方法有着更优的控制性能。

关键词: 智能汽车, 横向控制, 多模型切换, 硬件在环实验

Abstract:

Due to the complex and changeable road environment of the vehicle, the lateral dynamics of the vehicle enters the nonlinear region in some working conditions which means the lateral stiffness has a great change simultaneously. Aiming at the perturbation of the tire lateral stiffness, the lateral control model was
derived on the base of the structure of the lateral control of the intelligent vehicle. The lateral control method based on multi-model switching was proposed and the changeable lateral stiffness of the intelligent vehicle was regarded as the switch parameter. The robust lateral controller was carried out by the local robust controllers, which were switched with the robust control stable margin by the global controller. The Hardware-in-the-loop test was carried out. The results show that the multi-model switching method has better control performance than conventional methods.

Key words: intelligent vehicle, lateral control, multi-model switching control, hardware-in-the-loop test