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汽车安全与节能学报

JASE ›› 2020, Vol. 11 ›› Issue (1): 86-93.DOI: 10.3969/j.issn.1674-8484.2020.01.009

• 汽车安全 • 上一篇    下一篇

基于扰动观测的智能驾驶主动抗扰纵向车速控制算法

黄家宁，陈  韬 *，谢  辉，张国辉，阮迪望，闫  龙#br#   

  1. （内燃机燃烧学国家重点实验室，天津大学 机械工程学院，天津 300072，中国）
  • 收稿日期:2019-09-18 出版日期:2020-03-31 发布日期:2020-04-01
  • 通讯作者: 陈韬，副研究员。E-mail: tao.chen@tju.edu.cn。http://mail.tju.edu.cn/js6/javascript:void(0)
  • 作者简介:第一作者 / First author：黄家宁（1993—），男（汉），山东，硕士。E-mail: hjn2017@tju.edu.cn。
  • 基金资助:
    国家重点研发计划 (2016YFB0101402)。

Longitudinal vehicle speed control algorithm with active disturbance for intelligent driving based on disturbance observation

HUANG Jianing, CHEN Tao*, XIE Hui, ZHANG Guohui，RUAN Diwang, YAN Long   

  1. (State Key Laboratory of Engines, School of Mechanical Engineering of Tianjin University, Tianjin 300072, China)
  • Received:2019-09-18 Online:2020-03-31 Published:2020-04-01

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摘要： 为了减少智能驾驶车辆的纵向车速控制的时滞，提高主动抗扰性，提出一种基于扰动观测的 纵向车速控制算法，并进行了实车验证。模型中，采用前馈控制模块，并提前输出控制量，来提高车 速跟随的响应性；以主动抗扰控制（ADRC）模块作为反馈环节，采用扩张状态观测器（ESO）在线估计 内外部扰动，并在控制端进行补偿，实现了对车速的精确闭环控制。在弯道、环岛等路况下进行了实 车实验。结果表明：该算法可以在 5 s内控制车速从怠速快速跟踪到目标车速，总体平均误差为 0.17 km/h。因而，该算法较传统的比例积分微分 (PID) 有更好的响应性、控制精度和抗扰性。

关键词: 智能驾驶车辆 , 纵向车速控制 , 主动抗扰控制（ADRC）, 模型前馈 , 扩张状态观测器 (ESO) ,  实车验证

Abstract:  A longitudinal vehicle speed control algorithm was proposed based on disturbance observation and then verified by real vehicle to reduce the time lag of longitudinal speed control for intelligent driving vehicles and to improve its active immunity. The model used a feed-forward control module with control amounts being output in advance to improve the responsiveness of the vehicle speed following; using an active disturbance rejection control (ADRC) module as feed-back link, using an extended state observer (ESO) to estimate the internal and the external disturbances online. And performed compensation at the control end to achieve accurate closed-loop control for vehicle speed. The real vehicle tests were under the conditions of curved roads and roundabouts. The results show that the algorithm controls the vehicle speed to quickly track from idle speed to the target speed within 5 s. The overall average error is 0.17 km/h. Therefore, the algorithm has a better responsiveness, a better accuracy, and a better disturbance rejection than those by a traditional PID (proportion integration differentiation) method.

Key words: intelligent driving vehicles , longitudinal speed control ,  active disturbance rejection control (ADRC) , model feed-forward; , extended state observer (ESO) ,  real vehicle tests