基于H_(∞)观测器和迭代学习的泊车路径跟踪横向控制  

Lateral Control Method for Parking Path Tracking Based on H_(∞)Observer and Iterative Learning

作　　者：王杰 刘丛志 张澧桐 WANG Jie;LIU Congzhi;ZHANG Litong(Changchun University of Science and Technology,Changchun 130022,China;Chongqing University,Chongqing 400044,China)

机构地区：[1]长春理工大学,长春130022 [2]重庆大学,重庆400044

出　　处：《汽车工程学报》2025年第1期69-80,共12页Chinese Journal of Automotive Engineering

基　　金：国家自然科学基金项目(52102444);清华大学汽车安全与节能国家重点实验室开放基金项目(KFY2205);河北省中央引导地方科技发展基金项目(226Z2204G)。

摘　　要：泊车跟踪精度直接影响泊车效率、剩余泊车空间甚至泊车安全。当前,自动泊车路径跟踪绝大部分采用基于模型的反馈控制,模型参数的不确定性会导致泊车路径跟踪算法控制性能下降,进而产生较大的跟踪误差。为减小模型参数不确定性对泊车路径跟踪效果的影响,提出了基于迭代学习的前馈控制策略。考虑到在时间域对系统进行迭代学习控制通常受执行器实际速度的影响,所以控制时将系统由时间域转换到与期望路径相关的空间域。由于系统模型中的一些状态变量难以测量,且系统无法满足D型迭代学习率收敛条件,所以提出H_(∞)观测器设计准则以准确估计状态信息。同时,构造具有观测误差的增广系统以进行迭代学习控制,在线性二次型最优控制(LQR)的初次泊车跟踪信息基础上进一步减小泊车路径跟踪误差。进行硬件在环(HIL)测试,验证了该方法具有良好的实际应用潜力。试验结果表明,经过多次迭代后,与LQR初次控制的泊车跟踪效果相比,所提出的控制方法能更准确地跟踪期望路径。Parking tracking accuracy directly affects parking safety,efficiency,and available parking space.Currently,most autonomous parking path tracking relies on model-based feedback control.High tracking errors can arise from a decline in the algorithm's control performance due to uncertainties in system model parameters.In this paper,a feedforward control approach based on iterative learning was developed to reduce the impact of model parameter uncertainty on parking path tracking.Considering that iterative learning control of the system in the time domain was usually affected by the actual speed of the actuator,the system was transformed from the time domain to the space domain,which was related to the desired path.Due to the difficulty in measuring some state variables in the system model and the system's failure to meet the D-type iterative learning rate convergence condition,the design criteria for an H_(∞)observer were proposed to accurately estimate state information.Meanwhile,an augmented system with observation errors was constructed to implement iterative learning control,which further reduced the parking path tracking error based on the initial parking tracking information from linear quadratic optimal control(LQR).Finally,a hardware-in-the-loop(HIL)test was established,which proved that the proposed method had excellent practical application potential.The experimental results show that after several iterations,the proposed control method tracks the desired path more accurately than the initial LQR control.

关 键 词：路径跟踪 迭代学习 H_(∞)观测器 自动泊车 

分 类 号：U469.72[机械工程—车辆工程]