Path following control of underground mining articulated vehicle based on the preview control method

Due to the narrow roadway and poor working environment, underground mines pose a threat to the safety of vehicle drivers. The realization of automatic driving of underground mine vehicles can improve mining automation and intelligence and ensure safety of workers, and it can significantly increase mining and exploitation efficiency. Automatic driving of underground mining vehicles requires the technologies of location, communication, navigation, and path following control. Automatic driving of mining vehicles is the ultimate approach of autonomous navigation and auto driving, while path following control system is one of the core technologies of the autopilot system. The path following control system is a multi-variable, multi-constraint system. There are optimization problems under multiple constraints as well as challenges such as actuator saturation during the control process. To solve the above problems, a model predictive control method was introduced in this paper. By considering the relationship between the position and situation of the vehicle, the objective function of the predictive control was optimized by minimizing the lateral deviation of the following path and the heading angle deviation of the vehicle. Therefore, the optimal controls of vehicle speed and articulation angle were obtained, and the problem of multi-variable and multi-constraint system was solved. For the tracking overshoot problem caused by the inability of determining sudden changes of road curvature in the model predictive control strategy, a control method based on preview distance was proposed; thus, the vehicle path following control accuracy and stability was improved through the advance judgment of road mutation information. Matlab/Adams simulation software was used to perform a comparison simulation test. The results show that the model predictive following controller is capable of solving the control problem in multi-variable, multi-constrained system and effectively prevent the actuator saturation. Moreover, the model predictive following control strategy based on the preview distance keeps the horizontal deviation of the vehicle within ±0.04 m and the heading angle deviation within ±1.8°. Compared with the controller before improvement, the lateral position deviation is reduced by 80.9%, and the heading angle deviation is reduced by 59.1%; this proves that the improved controller has better lateral stability and accuracy.