Robust direct adaptive controller for a class of uncertain nonlinear systems using Petri Type 2 Fuzzy Neural Networks (PT2FNN) as a new approximator

Abstract

In this work, we consider the application of an Intelligent Petri Type 2 Fuzzy Direct Adaptive Control for a class of single input single output nonlinear systems. Within this scheme, the Petri Type 2 Fuzzy Neural Networks (PT2FNN) are employed to approximate an unknown ideal controller, that can achieve control objectives in the presence of external disturbance with high accuracy, minimum cost and global stability. The adjusted parameters of PT2FNN are updated online with a stable adaptation mechanism designed to minimize the tracking error. Stability of the proposed control scheme is shown based on Lyapunov theory. The simulation results showed that the developed controller which is based on Petri Type 2 Fuzzy Neural Networks (PT2FNN) performs successfully. Compared with type 1 and type 2 fuzzy adaptive controllers, the suggested Approximator can be used to design an efficient robust direct adaptive controller.