Modeling of unstructured uncertainties and robust controlling of nonlinear dynamic systems based on type-2 fuzzy basis function networks

Abstract

This paper proposes new methods for modeling unstructured uncertainties and robust controlling of unknown nonlinear dynamic systems by using a novel robust Takagi Sugeno fuzzy controller (RTSFC). First, a new training algorithm for an interval type-2 fuzzy basis function network (FBFN) is proposed. Next, a novel technique is presented to convert the interval type-2 FBFN to an interval type-2 Takagi Sugeno (TS) fuzzy model. Based on the interval type-2 TS and type-2 FBFN models, a robust controller is presented with an adjustable convergence rate. Since the type-2 fuzzy model with its new training technique can effectively capture the unstructured uncertainties and accurately estimate the upper and lower bounds of unknown nonlinear dynamic systems, the stability condition of the proposed control system is much less conservative than other robust control methods that are based on norm bounded uncertainties. Simulation results on an electrohydraulic actuator show that the RTSFC can reduce steady state error under different conditions while maintaining better responses than the other robust sliding mode controllers.