Optimal sliding mode type-2 TSK fuzzy control of a 2-DOF helicopter

Modeling stage of complex aerial vehicles requires tremendous man power and expertise because of their highly nonlinear dynamics as well as complex inter couplings. In this paper, we investigate a model free controller design which benefits from type-2 fuzzy neural networks with elliptic type-2 fuzzy membership functions to control a 2-DOF helicopter without the need of a priori knowledge about the mathematical model for the system. In order to train the parameters of the consequent part of the type-2 fuzzy neural network, a cost function based on the integral of the square of the sliding surface is defined. The solution of this cost function is an optimal training algorithm for the parameters of the consequent part of the type-2 fuzzy neural network. The simulation results show that having neither a priori knowledge about the mathematical model of the system nor its parameters, the proposed control algorithm is able to track the reference signals for both yaw and pitch angles by eliminating the steady state error. In addition, the simulation results show the superiority of the proposed controller over its type-1 counterpart in the presence of measurement noise in the system.