This paper aims to develop a novel fixed-time Lyapunov-based model predictive control (FTLMPC) scheme for the trajectory tracking control of autonomous surface vessels (ASVs) to improve the trajectory tracking performance. It is worth emphasizing that fixed-time control (FTC) is first emeging into the LMPC framework. By introducing the fixed-time auxiliary control system within the contraction constraints of Lyapunov-based model predictive control (LMPC) framework, the control performance of trajectory tracking is significantly improved, theoretically achieving fixed-time convergence. Mean-while, taking into account the practical constraints of the actuator thrusts, the input constraints are incorporated into the control strategy, explicitly defining a feasible attraction domain that ensures fixed-time stability. Simulation results validate that this method exhibits excellent control performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Trajectory Tracking Control of Autonomous Surface Vehicles Using Fixed-Time Lyapunov-Based Model Predictive Control


    Contributors:
    Zhou, Yuxing (author) / Hao, Li-Ying (author) / Wang, Run-Zhi (author)


    Publication date :

    2024-12-08


    Size :

    1078839 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Trajectory tracking control of autonomous vehicles based on event‐triggered model predictive control

    Jindou Zhang / Zhiwen Wang / Long Li et al. | DOAJ | 2024

    Free access

    Trajectory Tracking for High-Performance Autonomous Vehicles with Real-Time Model Predictive Control

    Pierini, Matteo / Fusco, Paolo / Senofieni, Rodrigo et al. | Springer Verlag | 2024

    Free access


    Trajectory Tracking Control of Autonomous Vehicles Combining Model Predictive Control and Dynamic Programming

    Luo, Xi / Ramirez-Mendoza, Ricardo A. / Wang, Jianhong et al. | IEEE | 2023