Accurate estimation of lateral dynamic states is crucial for the stability control of distributed drive electric vehicles (DDEVs), particularly under non-Gaussian noise conditions. This paper presents an integrated framework for sensor signal processing and vehicle stability control. A novel maximum correntropy square-root cubature Kalman filter (MCSCKF) is proposed to achieve robust estimation of key lateral dynamic variables, including sideslip angle and yaw rate, from noisy sensor measurements. By incorporating higher-order statistical characteristics into the filtering process, the proposed estimator enhances robustness against impulsive disturbances and modeling uncertainties. Based on the improved state estimation performance, a hierarchical direct yaw moment control (DYC) scheme is designed. The upper control layer employs a non-singular terminal sliding mode (NTSM) controller to generate yaw moment commands, while the lower layer allocates driving torques to individual in-wheel motors. The effectiveness of the proposed stability control strategy is validated through CarSim/Simulink co-simulations. In addition, real-vehicle experiments are conducted to evaluate the robustness and noise suppression capability of the proposed MCSCKF under practical driving conditions using a VBOX3i measurement system. The results demonstrate that the proposed estimator provides more stable and robust state estimation under non-Gaussian disturbances, which is beneficial for subsequent control applications.
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