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作 者:王玉琼[1] 高松[1] 王玉海[2,3] 徐艺 郭栋[1] 周英超[1] WANG Yu-qiong;GAO Song;WANG Yu-hai;XU Yi;GUO Dong;ZHOU Ying-chao(School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo 255000,China;State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130025,China;Qingdao Automotive Research Institute,Jilin University,Qingdao 266043,China)
机构地区:[1]山东理工大学交通与车辆工程学院,山东淄博255000 [2]吉林大学汽车仿真与控制国家重点实验室,吉林长春130025 [3]吉林大学青岛汽车研究院,山东青岛266043
出 处:《浙江大学学报(工学版)》2021年第10期1922-1929,1947,共9页Journal of Zhejiang University:Engineering Science
基 金:国家自然科学基金资助项目(51905320);山东省重大科技创新工程资助项目(2019JZZY010911).
摘 要:针对高速无人驾驶车辆运动控制过程中轨迹跟踪精度和稳定性难以同时保障的问题,提出综合前馈-反馈及自抗扰控制(ADRC)补偿相结合的横向控制算法.通过车速和道路曲率信息计算前馈稳态前轮转向角,将质心侧偏角引入航向偏差,以车辆航向角偏差和侧向偏差作为参考量进行反馈控制,通过前馈-反馈控制提升瞬态轨迹跟踪性能.设计自抗扰控制器,通过扩张状态观测器对未建模动态和内外界干扰进行估计,通过将后轮侧偏角控制在参考值附近来补偿前轮转角,提升无人驾驶车辆的转向稳定性和控制器的鲁棒性.不同工况下的仿真结果表明,利用该方法可以保证高速无人驾驶车辆稳定地跟踪期望路径行驶,轨迹跟踪偏差较小,对车辆参数变化和外界干扰具有较强的鲁棒性.A lateral control algorithm combining feedforward-feedback control and active disturbance rejection control(ADRC)was proposed aiming at the difficulty in ensuring the trajectory tracking accuracy and stability of high-speed autonomous vehicles.The feedforward steady-state front wheel steering angle was calculated by the vehicle longitudinal velocity and road curvature information,and the vehicle sideslip angle was introduced into the heading angle.Then the vehicle heading angle deviation and lateral deviation were used as the reference values for feedback control.The feedforward-feedback control was employed to improve vehicle transient trajectory tracking performance.Then the ADRC controller was designed,and the un-modeled dynamics along with the internal and external disturbances were estimated by the extended state observer.The sideslip angle of rear wheel was controlled near the reference value to compensate the front wheel steering angle by ADRC,which improved the steering stability of the autonomous vehicle and the robustness of the controller.The simulation results under different working conditions show that the proposed method can ensure the autonomous vehicle to stably track the desired trajectory with lower tracking deviation and has good robustness against parameter uncertainties and external disturbances.
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