Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor

TL;DR

This paper develops a model predictive control approach to improve the trajectory tracking of autonomous tractors by identifying yaw dynamics and implementing a control scheme that minimizes tracking errors.

Contribution

It introduces a novel control framework combining system identification and model predictive control for autonomous tractor trajectory tracking.

Findings

Euclidean error below 40 cm for straight trajectories

Euclidean error below 60 cm for curved trajectories

Demonstrates accuracy and efficiency of the proposed control scheme

Abstract

More efficient agricultural machinery is needed as agricultural areas become more limited and energy and labor costs increase. To increase their efficiency, trajectory tracking problem of an autonomous tractor, as an agricultural production machine, has been investigated in this study. As a widely used model-based approach, model predictive control is preferred in this paper to control the yaw dynamics of the tractor which can deal with the constraints on the states and the actuators in a system. The yaw dynamics is identified by using nonlinear least squares frequency domain system identification. The speed is controlled by a proportional-integral-derivative controller and a kinematic trajectory controller is used to calculate the desired speed and the desired yaw rate signals for the subsystems in order to minimize the tracking errors in both the longitudinal and transversal directions. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the euclidean error is below $40$ cm for time-based straight line trajectories and $60$ cm for time-based curved line trajectories, respectively.