A coordinated control strategy integrating Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) has been designed to enhance the trajectory tracking accuracy and stability of autonomous vehicles. This strategy consists of upper and lower-level controllers, with the upper level responsible for tracking desired trajectories and vehicle speed while minimizing lateral error. The lower-level control executes actions to achieve desired steering angles and accelerations. An Extended Kalman Filter (EKF) observer is employed to update the vehicle state. Finally, hardware-in-the-loop testing experiments are conducted to validate the robustness and effectiveness of the controller, which effectively reduces lateral error and improves tracking accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on trajectory tracking control algorithms for autonomous vehicles


    Contributors:

    Conference:

    Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024) ; 2024 ; Kuala Lumpur, Malaysia


    Published in:

    Proc. SPIE ; 13250


    Publication date :

    2024-08-23





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    LSTM-Based Trajectory Tracking Control for Autonomous Vehicles

    Yu, Jia / Zhang, Ming / Chen, ShiChang et al. | SAE Technical Papers | 2022


    Implementing Trajectory Tracking Control Algorithm for Autonomous Vehicles

    Duan, Xuting / Wang, Qi / Tian, Daxin et al. | IEEE | 2021



    LSTM-Based Trajectory Tracking Control for Autonomous Vehicles

    Chen, ShiChang / Yin, Zhishuai / Yu, Jia et al. | British Library Conference Proceedings | 2022


    Adaptive Finite Time Trajectory Tracking Control of Autonomous Vehicles

    Zhang, Ting / Xue, Jiaqi / Jiao, Xiaohong et al. | IEEE | 2020