  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.
