Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for those commercial vehicles that require higher regulation precision of braking force on four wheels when braking force distribution is carried out under some conditions. Due to the lagging information acquisition, most feedback-based control algorithms are difficult to further improve the dynamic response of PEBS. Meanwhile, feedforward-based control algorithms like predictive control perform well in improving dynamic performance but because of the large amount of computation and complexity of this kind of control algorithm, it cannot be applied in real-time on the single-chip microcomputer, and it is still in the stage of theoretical research at present. To address this issue and for the sake of engineering reliability, this article presents a logic threshold control scheme combining analogous model predictive control (AMPC) and proportional control. In addition, an experimental device for real-time measuring PEBS multi-dynamic parameters is built. After correcting the key parameters, the precise model is determined and the influence of switching solenoid valve on its dynamic response characteristics is studied. For the control scheme, numerical and physical validation is executed to demonstrate the feasibility of the strategy and for the performance of the controller design. The experimental results show that the dynamic model of PEBS can accurately reflect its pressure characteristics. Furthermore, under different air source pressures, the designed controller can stably control the pressure output of PEBS and ensure that the error is within 0.08 bar. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.


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    Title :

    Research on accurate modeling and control for pneumatic electric braking system of commercial vehicle based on multi-dynamic parameters measurement


    Contributors:
    Zhao, Yongtao (author) / Yang, Yiyong (author) / Wu, Xiuheng (author) / Tao, Xingjun (author)


    Publication date :

    2023-03-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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