Feedback error learning-based type-2 fuzzy neural network predictive controller for a class of nonlinear input delay systems

Abstract

In this paper, a type-2 fuzzy neural network predictive (T2FNNP) controller has been designed in the feedback error learning (FEL) framework for a class of input delay nonlinear systems considering of unknown disturbance and uncertainties. In this method, the predictor has been utilized to estimate the future state variables of the controlled system to compensate for the time-varying input delay. To establish the control objectives, the predicted states are fed to the controller which is in FEL framework and includes T2FNN parallel with classical feedback controller. Using the proposed T2FNNP controller, it is shown that the system output tracks the reference signal in presence of the measurement noise, time-varying delay, and bounded disturbance. An appropriate Lyapunov function has been exploited to study stability of the closed-loop system and to derive the adaptation laws for both the predictor and controller. A flexible joint robot system has been used to validate performance of the proposed T2FNNP controller and has been compared with a type-1 fuzzy sliding predictive controller through some simulations. The results indicate the efficiency of the proposed T2FNNP controller in dealing with time-varying delay as well as high level of measurement noise which exists in the sensor.

PMSM speed control based on intelligent sliding mode technique

“…In the past decade, type-2 fuzzy logic has been studied with more functionality and more flexibility than type-1 fuzzy logic (Tavoosi et al, 2016a;Tavoosi et al, 2016b). Type-2 fuzzy system has attracted much attention (Abiyev et al, 2011;Tavoosi et al, 2017;Pourasad et al, 2016;Sabahi et al, 2019;Lin et al, 2014;Tavoosi et al, 2016c;Chen and Wang, 2019). In Li et al (2015a), a new method to reduce the order of type-2 fuzzy systems has been proposed.…”

A Systematic Design of Stabilizer Controller for Interval Type-2 TSK Fuzzy Logic Systems

“…Therefore, the type-2 fuzzy sets were introduced by Zadeh to account for uncertainties in fuzzy models [8]. In recent years, the Type-2 Fuzzy Logic Systems (T2 FLSs) have been successfully applied in many areas due to their ability in the modelling of uncertainties [9][10][11][12][13][14]. A T2 FLS is very similar to a Type-1 Fuzzy Logic System (T1 FLS) and the major structural difference is the typereduction block, which is added to the structure of T2 FLS.…”