2D Path Following Control of Motor Graders

Abstract

Optimal ground conditions in mining are crucial for operational efficiency and safety. Motor graders play a crucial role in achieving precise grading of passages; however, unlike many other mining vehicles, they have not yet been commercially automated. The redundant kinematics of motor graders, including articulation, front axle steering, and blade operations, pose significant challenges for automation. This research explores path following with a 2D kinematic model of motor graders and uses simulation to evaluate and compare the performance of these controllers in terms of step response and operational effectiveness. To address kinematic redundancy, two methods are proposed: Rear Offset Control, which defines the articulation angle using a lateral offset between the front and rear axles, and Single Track Control, which utilizes steering redundancy to adapt existing automation approaches to articulated vehicles. Simulation tests were conducted using two controllers: a Feedback Linearized PD controller (FBL+PD) and a Feedback Linearized Model Predictive Controller (FBL+MPC). Both were tuned for optimal performance on a step input path, with performance assessed based on Root Mean Square Error (RMSE) and control effort. This work introduces two methodologies—Rear Offset Control and Single Track Control—for autonomous motor grader operation. These were implemented with FBL+PD and FBL+MPC controllers on a 2D kinematic model. Future research will focus on dynamic modeling, implementing a Non-Linear Model-Predictive Controller, refining tuning methodologies for Feedback Linearized systems, and integrating these approaches with live vehicles.