A robust on-line learning algorithm for type-2 fuzzy neural networks and its experimental evaluation on an autonomous tractor

Production machines, especially in agriculture, with higher efficiencies will be very important in the future because of the limited agricultural areas in the world and the high energy and labor costs. In order to increase the capacity of agricultural machinery, one can think to further increase the size of the machines. However, the limits in this direction will soon be reached as there is a maximum size to still allow road transport. On the other hand, energy costs are constantly increasing, such that the energy use should be minimized. A better option would be to use advanced learning algorithms, which can learn the system dynamics online, for the control of the production machines in order to increase their effectiveness. In this study, a Takagi-Sugeno-Kang type-2 fuzzy neural network with a sliding mode control theory-based learning algorithm is proposed for the control of the yaw dynamics of an autonomous tractor which includes various uncertainties, disturbances and nonlinearities, especially coming from the hydraulic sub systems. Experimental results show the efficacy and the efficiency of the proposed learning algorithm.