Adaptive Inverse Control of Nonlinear Dynamical System Using Type-2 Fuzzy Neural Networks

Type-2 fuzzy neural networks have a good ability in identification and control of nonlinear systems, time varying systems and also system with uncertainties. In this paper a new method for designing adaptive inverse type 2 fuzzy neural controllers for online control of nonlinear dynamical system has been introduced. The proposed network has seven layers that the first two layers consist of type-2 fuzzy neurons with uncertainty in mean of Gaussian membership functions, are used for fuzzification part. Third layer is the fuzzy rules layers. Reduction type is done in fourth layer with adaptive nodes. Reminder layers are used for consequent left–right firing points, two end-points and output of network respectively. In this paper, gradient descent with adaptive learning rate backpropagation is used for learning phase. Finally, Type-2 online Sugeno fuzzy neural network is used for tracking control of nonlinear dynamical water bath temperature system. Results are compared with Adaptive-Network-Based Fuzzy Inference System (ANFIS). Simulation results show the proposed method has a good efficiency.