Type-2 Fuzzy- Neural Controller Design

This paper presents an indirect adaptive type-2 fuzzy neural controller to control a class of nonlinear systems using feedback linearization theory and type-2 fuzzy neural networks. In this method, feedback linearization control input using two type-2 fuzzy neural networks is approximated. The gradient descent learning algorithm with adaptive learning rate is used for learning phase. In this paper, reduction in the number of fuzzy rules is proposed to simplify type-2 fuzzy neural network. By reducing the number of fuzzy rules, model complexity is diminished. Using Lyapunov stability theory, with the expression theorem, adaptation laws for tuning type-2 fuzzy neural networks is derived and stability of the closed loop system is guaranteed. Finally, the proposed controller is applied to twin-tank system in the presence of noisy data. Results are compared with Adaptive-Network-Based Fuzzy Inference System (ANFIS). Simulation results show the ability of the proposed method.