Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System

Abstract

One of the most critical tasks in tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the tractor and the trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

Cited by in corpus (6)

• Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles
• Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle
• Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor
• Tracking error learning control for precise mobile robot path tracking in outdoor environment
• Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers
• A Self-Learning Disturbance Observer for Nonlinear Systems in Feedback-Error Learning Scheme