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Automatic guidance of agricultural vehicles would lighten the job of the operator, while accuracy is needed to obtain an optimal yield. Accurately navigating a tractor consists of controlling different dynamic subsystems (steering and speed). Instead of modeling the subsystem interaction prior to model-based control, we have developed a control algorithm which learns the interactions on-line from the measured feedback error. In this approach, a PD controller is working in parallel with a type-2 fuzzy neural network. While the former ensures the stability of the related subsystem, the latter learns the system dynamics and becomes the leading controller. In this study, two combinations of a PD controller with a type-2 fuzzy neural network are implemented: one for the yaw dynamics and one for the traction dynamics. The interactions between these subsystems are thus not taken into account explicitly, but considered as disturbances to be handled by the subsystem controllers. A novel sliding mode control theory-based learning algorithm is used to train the type-2 fuzzy neural networks, and the convergence of the parameters is shown by using a Lyapunov function.