Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview

Abstract

In the view that lateral tracking accuracy of mine trucks is not high under the condition of complex path and muddy road, in order to improve the adaptability of the control algorithm to the complex driving environment in the mining area, an adaptive preview tracking control algorithm considering multi-factor fusion is proposed in this paper. Firstly, according to the vehicle kinematics, a rear wheel feedback tracking model based on preview is established. Secondly, vehicle trajectory is predicted by the trajectory prediction method. Considering the cumulative tracking error between reference trajectory and predicted trajectory, vehicle steering response delay and other factors, a multi-objective optimization function of adaptive preview is constructed. Finally, the optimization function is solved by genetic algorithms (GA), and the optimal preview point is output to the rear wheel feedback controller to realize the optimal control of the vehicle in the global path tracking process. Simulation and real vehicle test results show that when a vehicle starts with an initial lateral error of less than 0.8 m and a yaw angle error of 5°, the adaptive preview tracking control algorithm can achieve a certain degree of deviation-correcting control; the vehicle stops with a lateral error of less than 0.2 m and a yaw angle error of less than 2°, which effectively improves the tracking ability of mine trucks and its adaptability to complex working conditions.

Keywords

• adaptive preview
• unmanned driving
• path tracking
• trajectory prediction
• genetic algorithm
• mine truck