Intelligent weighted hierarchical direct yaw moment control for distributed drive electric vehicles

Distributed Drive Electric Vehicles (DDEVs) equipped with four-wheel independent drive (4WID) systems possess the capability to independently generate both driving and braking torques. However, in the context of Direct Yaw Moment Control (DYC), the dynamic coupling between yaw rate and sideslip angle remains a significant challenge for achieving effective coordinated control. To address this issue, a hierarchical control architecture is adopted, offering advantages such as structural flexibility and modularity. Within this framework, this paper proposes a Fast Adaptive Super-Twisting Sliding Mode Control (FASTSMC) strategy for DYC, and constructs an error model based on yaw rate and sideslip angle. To improve adaptability under varying driving conditions, a data-driven neural network with online updating based on phase plane stability parameters is integrated. This approach dynamically adjusts the weight coefficients in real time according to phase plane theory, thereby ensuring stable yaw moment control. Simulation results demonstrate that the proposed strategy reduces the sideslip angle by 54.7% under step input conditions and decreases the yaw rate by 22.4% during double-lane change conditions. These results confirm the effectiveness and superiority of the proposed control method.