In this paper, the seven degrees of freedom (DOF) nonlinear system model of articulated vehicles, including the vehicle dynamics model, tire and hydraulic steering system model, and the linearized ideal reference model, is constructed. A layered stability controller for the articulated vehicle is built. The particle swarm optimization (PSO)-based variable parameter linear quadratic regulator (LQR) for the upper-level yaw torque controller and the lower-level torque distributor based on the principle of the minimum tire utilization are established. The effectiveness of the LQR upper-level yaw torque controller for an articulated vehicle at different speeds and control references are compared and analyzed through feedforward and feedback control. We optimize the parameters in the LQR controller using PSO and verify the improvement in the controller’s performance with optimized parameters. Overall, the effect of front and rear-integrated control is best, followed by rear-based and front-based control. The PSO algorithm to optimize the LQR controller parameters for snaking oscillation control is effective.

Unmanned articulated vehicles play a crucial role in the intelligent mine system and have been extensively investigated and implemented in the fields of mine transportation, agriculture and forestry construction. However, the working environment of articulated wheeled vehicles is harsh and the working conditions are changeable. These conditions are often accompanied by load changes, road interference excitation caused by an unstructured environment and the dynamic nonlinear characteristics of articulated wheeled vehicles. The current research on path tracking control methods suitable for traditional wheeled vehicles does not meet the intelligent operation requirements of articulated wheeled vehicles, and it is necessary to combine the specific working environment and its own specific structural model characteristics. In this paper, a composite barrier function sliding mode control method based on an extended state observer is proposed to solve the problem of modeling uncertainty and unknown external disturbance in the path tracking control of unmanned articulated vehicles. Firstly, the mathematical model of the articulated wheeled working vehicle is built to derive the expected heading angle in the prediction horizon. Then, the strong nonlinear lumped disturbance in articulated dynamics is dynamically estimated by combining the composite nonlinear extended state observer. Afterward, based on the error compensation theory, a composite barrier function sliding mode controller suitable for articulated vehicle path tracking is derived. Finally, through simulation analysis and experimental verification, this method can estimate the strong nonlinear lumped disturbance caused by the structural characteristics of the articulated vehicle, and then compensate for the disturbance of the control quantity to achieve stable, robust and accurate path tracking control.