Adaptive tracking control for nonlinear systems by an adaptive model-based FNNs sliding mode control scheme

Abstract

This paper concerned with the adaptive tracking control problem of an adaptive model-based fuzzy-neural networks (FNNs) sliding mode control (AFSMC) scheme for a class of nonlinear systems, which are represented by Takagi-Sugeno (T-S) fuzzy model to express a nonlinear systems model. Then, an adaptive parameter estimator is proposed to estimate the unknown nonlinear system parameters. Considering the online estimating error from the estimation model and nonlinear system model, a state estimation based feedback controller is derived by the proposed adaptive FNNs sliding mode control scheme and free parameters can be updated online by adaptive laws based on Lyapunov stability theorem. The proposed control scheme can guarantee that the unknown nonlinear system output can track to the states of reference model for any desired input signals when the stability condition is satisfied. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed approaches.