Adaptive control for a class of nonlinear system using general type-2 fuzzy neural networks approximator

Abstract

In this paper, an indirect adaptive controller with general type-2 fuzzy neural networks (GT2FNN) approximator is proposed for a general class of SISO nonlinear systems. General type-2 fuzzy system (GT2FS) can be considered as a collection of numerous interval type-2 fuzzy systems (IT2FS) by α-plane. By using the recently introduced adaptive modulation factor into GT2FNN, the computational complexity and time-consuming are greatly reduced in the type reduction of IT2FS. The KM algorithm can be avoided in the type reduction of GT2FNN. Compared to the conventional GT2FNN, the proposed GT2FNN has obvious advantages in computational complexity and time consumption. The Lyaponov approach proves the stability of the closed-loop system. The simulation results show that the tracking performance of GT2FNN approximator is better than IT2FNN and T1FNN approximator.