In this paper, a state estimation method of distributed electric drive articulated vehicle dynamics parameters based on the forgetting factor unscented Kalman filter with singular value decomposition (SVD-UKF) is proposed. The 7DOF nonlinear dynamics model of a distributed electric drive articulated vehicle is established. The unscented Kalman filter algorithm is the foundation, with singular value decomposition replacing the Cholesky decomposition. A forgetting factor is introduced to dynamically adapt the observation noise covariance matrix in real time, resulting in a centralized parameter state estimator for the articulated vehicle. The proposed parameter state estimation method based on the forgetting factor SVD-UKF is simulated and compared with the unscented Kalman filter (UKF) estimation method. Key dynamic parameters are estimated, such as the lateral and longitudinal velocities and accelerations, angular velocity, articulated angle, wheel speeds, and longitudinal and lateral tire forces of both the front and rear vehicle bodies. The results show that the proposed forgetting factor SVD-UKF method outperforms the traditional UKF method. Furthermore, a prototype vehicle test is conducted to compare the estimated values of various dynamic parameters with the actual values, demonstrating minimal errors. This verifies the effectiveness of the proposed dynamic parameter estimation method for articulated vehicles.

This paper addresses the snaking instability of distributed electric-drive articulated vehicles under different speed conditions. A 7-DOF nonlinear vehicle model is established, and separate ideal reference models are constructed for the front and rear bodies. The particle swarm optimization is employed for offline optimization of the LQR weighting matrices, and a fuzzy adaptive PSO-LQR lateral stability control strategy is proposed. In this strategy, a fuzzy scheduling mechanism using vehicle speed and yaw rate as inputs is designed to adjust the LQR gains. Furthermore, a series of comparative simulations are conducted in this paper, and the simulation results are obtained under a fixed-speed sweep. Compared with the three-segment fixed-parameter LQR, the proposed method maintains lower articulated angles. At 11 m/s, the peak articulated angle is reduced by approximately 20%. On roads with adhesion coefficients of 0.2 and 0.8, fixed-parameter control suffers severe performance degradation. The proposed strategy maintains suppression performance on both friction surfaces. Comparative analyses with PSO linear interpolation and speed-only fuzzy control confirm the necessity of incorporating yaw rate as a second input. Further comparison with interval type-2 fuzzy control shows that both methods deliver comparable steady-state performance. The proposed method reduces computation time by approximately 56.1%.