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Application of Recursive Least Square Algorithm on Estimation of Vehicle Sideslip Angle and Road Friction

机译:递归最小二乘算法在车辆侧滑角度和道路摩擦估计中的应用

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摘要

A recursive least square (RLS) algorithm for estimation of vehicle sideslip angle and road friction coefficient is proposed. The algorithm uses the information from sensors onboard vehicle and control inputs from the control logic and is intended to provide the essential information for active safety systems such as active steering, direct yaw moment control, or their combination. Based on a simple two-degree-of-freedom (DOF) vehicle model, the algorithm minimizes the squared errors between estimated lateral acceleration and yaw acceleration of the vehicle and their measured values. The algorithm also utilizes available control inputs such as active steering angle and wheel brake torques. The proposed algorithm is evaluated using an 8-DOF full vehicle simulation model including all essential nonlinearities and an integrated active front steering and direct yaw moment control on dry and slippery roads.
机译:提出了一种递归最小二乘(RLS)估计车辆侧线角度和道路摩擦系数的算法。该算法使用来自传感器车载车辆的信息和来自控制逻辑的控制输入,旨在为主动安全系统提供必要的信息,例如主动转向,直接偏航时刻控制或其组合。基于简单的二维自由度(DOF)车辆模型,该算法最小化了车辆的估计横向加速度和横摆加速度之间的平方误差及其测量值。该算法还利用可用的控制输入,例如主动转向角和轮式制动扭矩。通过8-DOF全车仿真模型评估所提出的算法,包括所有必需的非线性和综合主动前转向和在干燥和滑溜溜道路上的直接横摆力量控制。

著录项

  • 作者

    Nenggen Ding; Saied Taheri;

  • 作者单位
  • 年度 2010
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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