Robust Adaptive Control of Uncertain Nonlinear Systems Using Fuzzy Logic Systems

Abstract

In this paper, we present a novel robust adaptive tracking control for a class of uncertain nonlinear system with uncertain system and gain functions, which are all unstructured (or non-repeatable) state-dependent unknown nonlinear functions. The Takagi-Sugeno type fuzzy logic systems are used to approximate uncertain functions and the robust adaptive fuzzy tracking control (RAFTC) algorithm is designed by use of Lyapunov theorem. The closed-loop system is proven to be semi-globally uniformly ultimately bounded. In addition, the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed and the adaptive mechanism with minimum-order dynamic compensator can be achieved. An example illustrating the proposed method is included for a dc motor connected to a gearbox with significant friction. Simulation results show the effectiveness of the control scheme