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

TL;DR

This paper presents a nonlinear model predictive control method for autonomous tractor-trailer systems, improving trajectory accuracy during field operations through combined state and parameter estimation.

Contribution

It introduces a centralized nonlinear MPC approach with moving horizon estimation for enhanced trajectory tracking in autonomous tractors.

Findings

Mean Euclidean error of 6.44 cm for tractor on straight lines

Mean Euclidean error of 3.61 cm for trailer on straight lines

Demonstrated effectiveness in curved trajectory tracking

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.