交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (3): 89-99.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于MPC 的大型车辆防侧翻控制方法

贺宜1,2,褚端峰*1,2,吴超仲1,2,严新平1,2   

  1. 1. 武汉理工大学智能交通系统研究中心,武汉430063; 2. 水路公路交通安全控制与装备教育部工程研究中心,武汉430063
  • 收稿日期:2015-02-10 修回日期:2015-04-14 出版日期:2015-06-25 发布日期:2015-06-29
  • 作者简介:贺宜(1986-),男,江西人,博士生.
  • 基金资助:

    国家自然科学基金资助(51105286);中央高校基本科研业务费专项基金资助(2014-IV-137);同济大学道路与交通工程教育部重点实验室开放基金资助(K201301);车路协同与安全控制北京市重点实验室开放基金资助(KFJJ-201401).

Anti-rollover Control for Heavy-duty Vehicles Based on Model Prodictive Control

HE Yi1,2, CHU Duan-feng1,2,WU Chao-zhong1,2, YAN Xin-ping1,2   

  1. 1. Intelligent Transportation Systems Research Center,Wuhan University of Technology,Wuhan 430063, China; 2. Engineering Research Center for Transportation Safety, Ministry of Education,Wuhan 430063, China
  • Received:2015-02-10 Revised:2015-04-14 Online:2015-06-25 Published:2015-06-29

摘要:

大型车辆由于其具有重心位置较高、质量较大且轮距相对较窄等特点,比其他车辆更易发生侧翻事故.本文通过建立大型车辆三自由度动力学模型,采用LTR侧翻评价指标,对侧翻状态进行预测.进而基于模型预测控制(Model Prodictive Control,MPC)方法建立车辆防侧翻控制系统的状态空间方程,并以侧偏角和横摆角速度作为状态变量,通过差速制动方式对车辆施加横摆力矩以保持行车稳定性.通过Trucksim 与MATLAB/ Simulink 联合仿真实验,对该控制算法在典型工况下进行验证.结果表明,防侧翻控制系统能有效抑制车辆发生侧翻,保障行车安全,且侧翻控制的实时性和有效性满足要求.

关键词: 智能交通, 侧翻控制, 模型预测控制, 大型车辆, 联合仿真

Abstract:

The heavy-duty vehicles have high gravity-center, narrow track and large size, therefore the rollover limit value is lower than other kinds of vehicles, which may easy to cause rollover accidents. A 3- DOF rollover and control model is established for HDVs and the LTR index is proposed to predict the vehicle rollover. Then, the state-space equation of anti-rollover control system is built based on model prediction control(PMC). The side slip angle and yaw rate is seen to be state variable in the control system. The additional yaw moment can be coordinated by differential braking control model. Finally, the program is compiled based on the Trucksim/Simulink platform. The results show that the anti- rollover control system proposed can prevent the vehicle rollover effectively and enhance the driving performance of the vehicle on effectiveness and timeliness.

Key words: intelligent transportation, anti-rollover control, model prediciton control, heavy-duty vehicles, combination simulation

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