Strong nonlinearities under extreme conditions pose intractable challenges for the motion control of Automated Vehicles (AVs). Incapable or inaccurate modeling of nonlinearities, coupled with enormous cost of nonlinear controls, severely limit stability and performance enhancements in these scenarios. This paper proposes a novel modeling and robust control framework to address these issues. First, a novel hybrid modeling approach for trajectory tracking of AVs, combining a prior nominal model and a data-driven uncertain model based on Koopman theory, is proposed to enhance model predictive ability effectively. The finite approximation of Koopman operators captures the intrinsic characteristics of the nonlinear AV system via linear evolution in lifted observable space. Second, a Koopman-based Tube Robust MPC (K-TRMPC) is developed based on the hybrid model and zonotopic set theory. Koopman modeling error raised by the finite operators is considered a disturbance of the perturbed system. Tube-based design for constraint-tightening is developed for the nominal and lifted systems to guarantee closed-loop robustness. A reachability analysis on the future evolution of the perturbed system proves its convergence. Finally, the proposed framework is validated on real-time experiments and simulations, confirming the improved tracking performance on various surface conditions and vehicle stability in combined-slip scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Koopman-Based Hybrid Modeling and Zonotopic Tube Robust MPC for Motion Control of Automated Vehicles


    Contributors:
    Zheng, Hao (author) / Li, Yinong (author) / Zheng, Ling (author) / Hashemi, Ehsan (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    19071922 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    A zonotopic FDI approach with LPV-based EKF in autonomous vehicles

    Conejo Barceló, Carlos / Puig Cayuela, Vicenç / Morcego Seix, Bernardo et al. | BASE | 2024

    Free access

    Deep Neural Networks With Koopman Operators for Modeling and Control of Autonomous Vehicles

    Xiao, Yongqian / Zhang, Xinglong / Xu, Xin et al. | IEEE | 2023


    Koopman-Operator Control Optimization for Relative Motion in Space

    Servadio, Simone / Armellin, Roberto / Linares, Richard | AIAA | 2023


    A Koopman-Operator Control Optimization for Relative Motion in Space

    Servadio, Simone / Armellin, Roberto / Linares, Richard | TIBKAT | 2023


    A Koopman-Operator Control Optimization for Relative Motion in Space

    Servadio, Simone / Armellin, Roberto / Linares, Richard | AIAA | 2023