Control and identification of dynamic plants using adaptive neuro-fuzzy type-2 strategy

The foremost objective of operative control for the unpredictable system is the design of proper and appropriate control system. The handling of insufficient information by using modern methods is of great importance. Therefore, this paper proposes the design of Type-2 fuzzy sets to deal with uncertainties in the unpredictable system in better and appropriate way as Type-2 fuzzy sets possess the capability of providing extra parameters and degree of freedom. Moreover, the construction of Adaptive Neuro-Fuzzy Type-2 (ANFT2) having a basic fuzzy set of rules is demonstrated. Gradient descent methodology is the basic method for parameter updating rules. The proposed scheme is experienced for both control and identification purpose through a commonly used dynamic system. It is observed that the projected ANFT2 structure gives better outcomes as compared to other control and identification techniques.