This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.


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    Titel :

    Lane Keeping Control of Autonomous Vehicles With Prescribed Performance Considering the Rollover Prevention and Input Saturation


    Beteiligte:
    Hu, Chuan (Autor:in) / Wang, Zhenfeng (Autor:in) / Qin, Yechen (Autor:in) / Huang, Yanjun (Autor:in) / Wang, Jinxiang (Autor:in) / Wang, Rongrong (Autor:in)


    Erscheinungsdatum :

    01.07.2020


    Format / Umfang :

    3954427 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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