A Longitudinal and Lateral Coordinated Control Method of Autonomous Vehicles Considering Time-Varying Delay

IF 14 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-26 DOI:10.1109/TIV.2024.3393983

Zhaobo Qin;Wang Liang;Zuoxu Zang;Liang Chen;Manjiang Hu;Qingjia Cui;Yougang Bian

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引用次数: 0

Abstract

In order to improve the control accuracy and stability of autonomous vehicles under large delay conditions, the paper proposes a longitudinal and lateral coordinated control method considering fixed sensor delay, actuator lag, and time-varying CAN communication delay. An adaptive all-pass filter time-delay estimator (AAPF-TDE) based on finite impulse response (FIR) and its improved strategies are designed to realize accurate online estimation of CAN communication delay. According to the estimated delay, a longitudinal and lateral coupling time-delay dynamics model with prediction revision and delay augmentation is constructed. A model predictive controller (MPC) combined with Lyapunov asymptotic stability constraints is then designed. CarSim/Simulink co-simulation and vehicle experiment results show that the proposed controller can improve the vehicle stability effectively while ensuring the longitudinal and lateral control accuracy under large delay conditions compared with the controller without considering delay or only considering fixed delay.

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考虑时变时滞的自动驾驶汽车纵向与横向协调控制方法

为了提高自动驾驶汽车在大延迟条件下的控制精度和稳定性，本文提出了一种考虑固定传感器延迟、执行器滞后和时变CAN通信延迟的纵向和横向协调控制方法。设计了一种基于有限脉冲响应（FIR）的自适应全通滤波器时延估计器（AAPF-TDE）及其改进策略，实现了CAN通信时延的在线准确估计。根据估计的时滞，构造了纵向和横向耦合时滞动力学模型，并进行了预测修正和时滞增广。然后设计了结合Lyapunov渐近稳定性约束的模型预测控制器（MPC）。CarSim/Simulink联合仿真和车辆实验结果表明，与不考虑延迟或只考虑固定延迟的控制器相比，所提出的控制器在保证大延迟条件下车辆纵向和横向控制精度的同时，能有效提高车辆的稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization

CiteScore

12.10

自引率

13.40%

发文量

177

期刊介绍： The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.