Sampled-data filtering of Takagi–Sugeno fuzzy neural networks with interval time-varying delays

Abstract

This paper is concerned with sample-data filtering of T–S fuzzy neural networks with interval time-varying delays, which is formed by a fuzzy plant with time delay and a sampled-data fuzzy controller connected in a closed loop. A Takagi–Sugeno (T–S) fuzzy model is adopted for the neural networks and the sampled-data fuzzy controller is designed for a T–S fuzzy system. To develop the guaranteed cost control, a new stability condition of the closed-loop system is guaranteed in the continuous-time Lyapunov sense, and its sufficient conditions are formulated in terms of linear matrix inequalities. By using a descriptor representation, the sampled-data fuzzy control system with time delay can be reduced to ease the stability analysis, which effectively leads to a smaller number of LMI-stability conditions. Information of the membership functions of both the fuzzy plant model and fuzzy controller are considered, which allows arbitrary matrices to be introduced, to ease the satisfaction of the stability conditions. By a newly proposed inequality bounding technique, the fuzzy sampled-data filtering performance analysis is carried out such that the resultant neural networks is asymptotically stable. Numerical example and simulation result are given to illustrate the usefulness and effectiveness of the proposed theoretical results.