When distributed drive electric vehicles (DDEVs) encounter challenges such as congested parking, abrupt dead ends, and narrow roads, in situ steering provides an excellent solution. However, the operational mechanisms and control strategies of in situ steering are currently a gap in distributed drive technology. To achieve stable and precise control of in situ steering, this article proposes an adaptive control strategy for synchronizing yaw rate and wheel speed, based on the analysis of the kinematics and dynamics of in situ steering. The control strategy is structured in layers, comprising an observation layer, a decision-making layer, and upper layer control. Initially, the observation layer calculates the road adhesion coefficient and slip ratio, using a dynamics-based method to assess road conditions at the onset of in situ steering. Subsequently, the decision-making layer determines the nominal yaw rate and the desired wheel speed. Then, the upper layer control designs an active disturbance rejection control (ADRC) algorithm, based on the tire force characteristics of in situ steering, along with a multisegment linear dynamic learning rate adaptive control algorithm, to precisely control the yaw rate and wheel speed while minimizing steering center deviation. In addition, a collaborative control submodule coordinates torque and braking pressure based on the designed state determination criteria, enhancing the system’s robustness and safety. Ultimately, simulations and real-vehicle tests validate the effectiveness of the proposed in situ steering mechanisms and control strategy design, confirming that the designed control strategy achieves satisfactory performance.


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    Title :

    A Hierarchical Cooperative Control Strategy for In Situ Steering of Distributed Drive Electric Vehicle


    Contributors:
    Lv, Haoyu (author) / Wang, Xiangyu (author) / Xie, Bin (author) / Shi, Biaofei (author) / Li, Quantong (author) / Xu, Yinggang (author) / Cheng, Luhua (author)


    Publication date :

    2025-06-01


    Size :

    3325931 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English