Maintaining both path-tracking accuracy and yaw stability of distributed drive electric vehicles (DDEVs) under various driving conditions presents a significant challenge in the field of vehicle control. To address this limitation, a coordinated control strategy that integrates adaptive model predictive control (AMPC) path-tracking control and direct yaw moment control (DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchical framework, is coordinated by the upper layer of path-tracking control and the lower layer of direct yaw moment control. Based on the linear time-varying model predictive control (LTV MPC) algorithm, the effects of prediction horizon and weight coefficients on the path-tracking accuracy and yaw stability of the vehicle are compared and analyzed first. According to the aforementioned analysis, an AMPC path-tracking controller with variable prediction horizon and weight coefficients is designed considering the vehicle speed's variation in the upper layer. The lower layer involves DYC based on the linear quadratic regulator (LQR) technique. Specifically, the intervention rule of DYC is determined by the threshold of the yaw rate error and the phase diagram of the sideslip angle. Extensive simulation experiments are conducted to evaluate the proposed coordinated control strategy under different driving conditions. The results show that, under variable speed and low adhesion conditions, the vehicle's yaw stability and path-tracking accuracy have been improved by 21.58\% and 14.43\%, respectively, compared to AMPC. Similarly, under high speed and low adhesion conditions, the vehicle's yaw stability and path-tracking accuracy have been improved by 44.30\% and 14.25\%, respectively, compared to the coordination of LTV MPC and DYC. The results indicate that the proposed adaptive path-tracking controller is effective across different speeds.

翻译：在各种行驶工况下保持分布式驱动电动汽车（DDEVs）的路径跟踪精度和横摆稳定性，是车辆控制领域的一项重大挑战。为解决这一不足，本文针对DDEVs提出了一种集成自适应模型预测控制（AMPC）路径跟踪控制与直接横摆力矩控制（DYC）的协调控制策略。该策略受分层框架启发，通过上层路径跟踪控制与下层直接横摆力矩控制实现协调。首先，基于线性时变模型预测控制（LTV MPC）算法，比较并分析了预测时域和权重系数对车辆路径跟踪精度和横摆稳定性的影响。根据上述分析，在上层考虑车速变化，设计了具有可变预测时域和权重系数的AMPC路径跟踪控制器。下层则涉及基于线性二次型调节器（LQR）技术的DYC。具体而言，DYC的介入规则由横摆角速度误差阈值和质心侧偏角相图共同决定。通过大量仿真实验，评估了所提协调控制策略在不同行驶工况下的性能。结果表明：在变速和低附着条件下，车辆的横摆稳定性和路径跟踪精度较AMPC分别提升了21.58%和14.43%；同样，在高速度和低附着条件下，车辆的横摆稳定性和路径跟踪精度较LTV MPC与DYC协调控制分别提升了44.30%和14.25%。这表明所提出的自适应路径跟踪控制器在不同车速下均具有有效性。