A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems

This paper presents a new method for comprehensive stabilization and backstepping control system design class of stochastic nonlinear systems. These types systems are so abundant in practice that the designer must assume random noise with definite probability distribution affects dynamics observations state variables. Stochastic is intended to determine time course variables target achievable even minimal cost. Since mathematical equations not always constant, every model-based controller can be accurate. Therefore, this paper, type-3 fuzzy neural network used estimate parameters method. In simulation, proposed compared Type-1 RBFN methods. Results clearly show has very good performance any class.