Adaptive Fuzzy Sliding-Mode Control of Uncertain Nonlinear System

Abstract

This paper presents an adaptive sliding-mode control algorithm for uncertain nonlinear system. It is very difficult to obtain the exact knowledge and it is required to approximate an unmolded dynamics with a nonlinear component. Therefore, a fuzzy basis function network is applied to approximate the unknown dynamics of nonlinear system. The paper employs a weight factor to adjust the ratio of direct and indirect adaptive fuzzy control, meantime a supervisory controller is introduced and a sliding-mode controller is to ensure optimal tracking performance of the closed-loop system. To prevent the system state variables unpredictable, this paper designs an observer to estimate the unpredictable states. The control structure and learning rules are derived from a Lyapunov theory extension that guarantees both tracking errors and parameter estimate errors in the closed-loop system are bounded. A two-arm robot is simulated to verify the feasibility of the proposed control scheme.