Fuzzy logic is a subset of the soft computing that gives the ability to make decisions in uncertain conditions to computer systems. Today, the fuzzy expert systems are successful in some states such as making decisions in the conditions of uncertainty and control of the complex systems. In a fuzzy system, it is very difficult to determine the exact membership degree, especially in unknown systems or highly nonlinear systems. This problem is solved by using type-2 fuzzy logic and type-2 fuzzy systems. In the type-2 fuzzy logic, membership degree is a fuzzy number. In recent years, type-2 fuzzy systems have been more attention because of more flexibility and capability in systems modeling in the high uncertainty condition. Fuzzy neural networks are a kind of hybrid intelligent systems obtained from fuzzy systems and neural networks. These structures have the learning ability of neural networks and inference ability of fuzzy systems. So they can be used for various applications. In recent years, type-1 fuzzy logic generalizes to type-2 fuzzy logic and type-1 fuzzy neural networks have been developed to type-2 fuzzy neural networks. In this thesis type-2 fuzzy logic and type-2 fuzzy systems is briefly introduced and various structures of type-2 fuzzy neural networks and their learning algorithm to control of nonlinear dynamic systems are reviewed. Due to the complex nature of type-2 fuzzy neural models than the polynomial models, to expand the use of type-2 fuzzy neural models, these models can be further simplified. In this thesis, the proposed method to simplify type-2 fuzzy neural network is to reduce the number of fuzzy rules. By reducing the number of fuzzy rules using manual and automated method, number of model parameters will be very low and the network training time will be reduced. Reduction in the number of rules when using type-2 fuzzy neural network in online identification and control will help greatly. Also two controller design methods including adaptive inverse control and indirect adaptive control using type-2 fuzzy neural networks are expressed. These controllers respectively for water temperature control system and twin-tank system are designed and their simulation results are investigated. Keywords: Adaptive Inverse Control, Indirect Adaptive Control, Type-2 Fuzzy Logic, Type-2 Fuzzy System, Type-2 Fuzzy Neural Network, Temperature Control System, Twin-Tank System.