Research on Trajectory Tracking Control for Automatic Driving of Articulated Scraper

To improve the trajectory tracking control performance of an articulated shovel for mining, a genetic algorithm-based model predictive control weight reformulation method is applied to the trajectory tracking and motion control of the articulated shovel to improve the response speed and control accuracy of the shovel. By establishing a nonlinear vehicle kinematic model satisfying the non-integrity constraint and linearizing it according to the desired trajectory; the model predictive controller weight coefficients are solved using a genetic algorithm with known vehicle internal states. The results show that the optimized weight coefficients are helpful to improve the response time and tracking accuracy of the articulated vehicle trajectory tracking and can better meet the requirements of control accuracy by comparing the simulation experiments with the model predictive controller.