Path Following Model Predictive Control for Center-Articulated Vehicles

Abstract: Increased safety and productivity are driving factors for the trend in the mining industry where equipment and machines increasingly get automated. An example is the load-haul-dump vehicle, which is a machine that is used for transport of ore in underground mines. The cyclic load-haul-dump process is well suited for automation and automated loaders are commercially available today. Recent advances in autonomous driving have raised questions if there are efficiency gains that can be made by improving the path following algorithms that are used in the control. The aim of this thesis is to investigate the usage of model predictive control for path following for center-articulated mining vehicles. Two path following nonlinear model predictive controllers are designed and implemented. One controller is based on an error dynamics model, formulated as a regulation problem and implemented with the open source NMPC-library GRAMPC. The second controller is based on a kinematic model, formulated as a reference tracking NMPC problem and implemented using the embedded-MPC software tool FORCESPRO. The controllers are simulated on the same hardware that is used in real load-haul-dump vehicles, in a simulation environment provided by Epiroc Rock Drills AB. The results from the simulations show that both controllers can successfully follow a path, with a similar level of path error and less aggressive control actions compared to the current path following controller. The implemented controllers perform the control computations within a range of milliseconds on the embedded hardware, which is fast enough for real-time operation of the load-haul-dump vehicle.