Quantized feedback fuzzy sliding mode control design via memory-based strategy

Abstract

This paper is concerned with the sliding mode control (SMC) design for a class of Takagi–Sugeno (T–S) fuzzy nonlinear systems subject to model uncertainties and input quantization mismatch. A novel memory-based sliding surface is presented which includes not only the current states but also the past state information of the systems. Sufficient conditions for the design of the switching gains are given via linear matrix inequality(LMI) technique, and then the reaching conditions of the sliding surface is constructed to ensure the reachability of the sliding manifold. Furthermore, an adaptive neuro-fuzzy inference system(ANFIS) is introduced for reducing the high-frequency chattering induced by the signum function term in the sliding mode control. The effectiveness of the proposed methodology is illustrated by Matlab simulations.