Model-Free Adaptive Model Predictive Control for Trajectory Tracking of Autonomous Mining Trucks.

Area of Science:

• Robotics and Control Systems
• Automotive Engineering
• Geotechnical Engineering

Background:

• Autonomous mining trucks require precise trajectory-tracking for efficient operations.
• Existing vehicle dynamics models fail to accurately represent complex tire-ground interactions in rugged open-pit mine environments.
• Linear models used in conventional control methods lead to degraded tracking performance.

Purpose of the Study:

• To develop an advanced trajectory-tracking control framework for autonomous mining trucks.
• To address the limitations of current models in capturing complex terrain interactions.
• To enhance the overall tracking performance and reliability of autonomous mining vehicles.

Main Methods:

• Integration of Model Predictive Control (MPC) with a warm-start strategy for computational efficiency.
• Incorporation of Model-Free Adaptive Control (MFAC) for real-time compensation of control deviations.
• Validation through co-simulations on CarSim and MATLAB/Simulink platforms under diverse conditions.

Main Results:

• The proposed MPC-MFAC framework significantly improves trajectory-tracking performance.
• Demonstrated substantial reductions in mean error (90.83%), maximum error (15.05%), and RMSE (71.93%) compared to LQR control.
• Achieved a computation time of only 2 ms for MPC, enhancing controller efficiency.

Conclusions:

• The novel integrated control framework effectively enhances the trajectory-tracking capabilities of autonomous mining trucks.
• The method provides robust performance across various road conditions and driving scenarios.
• This approach offers a promising solution for improving the efficiency and safety of autonomous mining operations.