In this paper, a data-driven non-model-based approach is proposed for the adaptive optimal control of a class of connected vehicles that is composed of n human-driven vehicles only transmitting motional data and an autonomous vehicle in the tail receiving the broadcasted data from preceding vehicles by wireless vehicle-to-vehicle (V2V) communication devices. Considering the cases of range-limited V2V communication and input saturation, several optimal control problems are formulated to minimize the errors of distance and velocity and to optimize the fuel usage. By employing an adaptive dynamic programming technique, the optimal controllers are obtained without relying on the knowledge of system dynamics. The effectiveness of the proposed approaches is demonstrated via the online learning control of the connected vehicles in Paramics' traffic microsimulation.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data-Driven Adaptive Optimal Control of Connected Vehicles




    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 / 55.24 / 55.84 Straßenverkehr / 55.24 Fahrzeugführung, Fahrtechnik



    Data-Driven Adaptive Optimal Control of Connected Vehicles

    Gao, Weinan / Jiang, Zhong-Ping / Ozbay, Kaan | IEEE | 2017



    Transportation systems with connected and non-connected vehicles: Optimal traffic control

    de Luca, Stefano / Di Pace, Roberta / Di Febbraro, Angela et al. | IEEE | 2017


    Robust Design of Connected Cruise Control Among Human-Driven Vehicles

    Hajdu, David / Ge, Jin I. / Insperger, Tamas et al. | IEEE | 2020


    Data-driven Predictive Connected Cruise Control

    Shen, Minghao / Orosz, Gabor | IEEE | 2023