Type-2 Fuzzy neural networks for sliding mode Fuzzy control of nonlinear dynamical systems with adaptive learning rate
Alireza Zarif Khoramdel Azad, Mojtaba Ahmadieh Khanesar, Mohammad Teshnehlab · 2013
This paper proposes a novel training method for type-2 fuzzy neural networks (T2FNN). The proposed control method benefits from a sliding mode training method with adaptive learning rate. The proposed control structure is a feedback error learning structure so consists of a conventional controller in parallel with a T2FNN The conventional controller is responsible to stabilize the system. The stability of the proposed training method and the adaptive learning rate is proved using an appropriate Lyapunov function. The adaptive learning rate makes it possible to control the system without prior knowledge about the upper bound of the states of the system and their derivatives. The proposed approach is tested on the velocity control of an electro hydraulic servo. The proposed controller is compared with type-1 fuzzy neural networks; it is shown that in the presence of noise type-2 fuzzy system outperforms its type-1 counterpart.