Recurrent interval type-2 neuro-fuzzy control of an electro hydraulic servo system

Abstract

This paper presents a recurrent interval type-2 neuro-fuzzy controller which benefits from a sliding mode theory-based training algorithm. The recurrent interval type-2 neuro-fuzzy benefits from recurrent type-2 membership functions with interval variances which are trained by a novel training method. Furthermore, the adaptation laws considered for the parameters of the controller benefit from an adaptive learning rate. The stability of the proposed training method is considered using an appropriate Lyapunov function. The proposed method is simulated on an electro hydraulic servo system. The results of simulations show that the proposed method can control the system with a satisfactory performance.