  Provision of some autonomous functions to an agricultural vehicle would
lighten the job of the operator but in doing so, the accuracy should not be
lost to still obtain an optimal yield. Autonomous navigation of an agricultural
vehicle involves the control of different dynamic subsystems, such as the yaw
angle dynamics and the longitudinal speed dynamics. In this study, a
proportional-integral-derivative controller is used to control the longitudinal
velocity of the tractor. For the control of the yaw angle dynamics, a
proportional-derivative controller works in parallel with a type-2 fuzzy neural
network. In such an arrangement, the former ensures the stability of the
related subsystem, while the latter learns the system dynamics and becomes the
leading controller. In this way, instead of modeling the interactions between
the subsystems prior to the design of model-based control, we develop a control
algorithm which learns the interactions online from the measured feedback
error. In addition to the control of the stated subsystems, a kinematic
controller is needed to correct the errors in both the x- and the y- axis for
the trajectory tracking problem of the tractor. To demonstrate the real-time
abilities of the proposed control scheme, an autonomous tractor is equipped
with the use of reasonably priced sensors and actuators. Experimental results
show the efficacy and efficiency of the proposed learning algorithm.
