China Mechanical Engineering

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Fault Diagnosis for SBW Systems of Unmanned Vehicles

XIONG Lu1,2;FU Zhiqiang2;LI Zengliang2;ZHANG Renxie1   

  1. 1.Automotive College,Tongji University,Shanghai,201804
    2.Sino-German School for Postgraduate Studies of Tongji University,Shanghai,201804
  • Online:2017-11-25 Published:2017-11-23
  • Supported by:
    National Key Technology R&D Program(No. 2015BAG17B01)

无人车的线控转向系统故障诊断

熊璐1,2;付志强2;李增良2;章仁燮1   

  1. 1.同济大学汽车学院,上海,201804
    2.同济大学中德学院,上海,201804
  • 基金资助:
    国家科技支撑计划资助项目(2015BAG17B01)
    National Key Technology R&D Program(No. 2015BAG17B01)

Abstract: In order to ensure the safety and reliability of SBW systems for unmanned vehicles, the structures, working principles and malfunctions of SBW were analyzed. Using discrete dynamics model and 2-DOF vehicle model, with yaw rate signals, lateral acceleration signals and the current signals of steering motor, a real-time fault diagnose algorithm was designed based on Kalman filter method for steering tube column corner sensor, and through real-time estimation of motor parameters on abrupt faults to diagnose real-time faults of the motor. Real vehicle test results show the fault diagnose algorithm proposed herein may timely and accurately diagnose the failures of SBW systems.

Key words: unmanned vehicle, steer-by-wire(SBW) system, Kalman filter, fault diagnosis

摘要: 针对无人车线控转向系统的安全性及可靠性问题,分析了它的结构组成、工作原理以及故障类型,并且利用线控转向系统离散动力学模型和车辆二自由度模型,借助横摆角速度、侧向加速度和转向执行电机电流信号,设计了基于卡尔曼滤波方法的对转向管柱转角传感器进行实时故障诊断的算法,针对电机的突变故障,通过对电机参数的实时估计来进行故障诊断。实车试验验证表明,所设计的故障诊断算法能够准确、及时诊断出无人车线控转向系统所出现的故障。

关键词: 无人车, 线控转向系统, 卡尔曼滤波器, 故障诊断

CLC Number: