基于扰动观测的智能驾驶主动抗扰纵向车速控制算法

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。

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

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.