Fuzzy Adaptive PSO-LQR Lateral Stability Control for Distributed Electric-Drive Articulated Vehicles Against Snaking Instability

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%.