Optimization Study of Trajectory Tracking Algorithm for Articulated Vehicles Based on Adaptive Sliding Mode Control - Rui Li; Lin Li; Tiezhu Zhang; Zehao Sun; Kehui Ma (World Electric Vehicle Journal)
Unmanned underground articulated dump trucks (UADTs) are an important direction for the coal mining industry to vigorously promote automation and intelligence. Among these, tracking and controlling the motion trajectory is the key weak link. This paper presents a kinematic analysis of the stationary turning process of UADTs. Then, a posture state model for articulated trucks is established. The objective is to optimize the control method and further improve trajectory tracking accuracy. Based on the advantages and disadvantages of the feedback linearization control (FLC) method, a sliding mode control method based on the Ackermann formula (ASMC) and integral type switch gain (ISMC) are proposed. Finally, hardware-in-the-loop simulation verifies the superiority and tracking quality of the controller. The results show that the ASMC controller can control the lateral position deviation, course angle deviation, and curvature deviation around 10 cm, 0.04 rad, and 0.08 m−1 in the hardware-in-the-loop simulation environment. The ISMC controller can control the lateral position deviation, course angle deviation, and curvature deviation near 8 cm, 0.01 rad, and 0.02 m−1, and can also effectively control the jitter problem. Each deviation is stabilized within 10 s. This provides a reference for the development of trajectory tracking strategies for articulated vehicles.