AUTHOR=Wang Shenquan , Chen Shuaiqi , Ji Wenchengyu , Liu Keping 

TITLE=Quantized Sampled-Data Control for T-S Fuzzy System Using Discontinuous LKF Approach

JOURNAL=Frontiers in Neuroscience

VOLUME=13

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00372

DOI=10.3389/fnins.2019.00372

ISSN=1662-453X

ABSTRACT=<p>In this study, the stability for a class of sampled-data Takagi-Sugeno (T-S) fuzzy systems with state quantization was investigated. Using the discontinuous Lyapunov-Krasoskii functional (LKF) approach and the free-matrix-based integral inequality bounds processing technique, a stability condition with less conservativeness has been obtained, and the controller of the sampled-data T-S fuzzy system with the quantized state has been designed. Furthermore, based on the results, the sampled-data T-S fuzzy system without the state quantization was also discussed, and the required controller constructed. The results of two simulation examples show that both the maximum sampling intervals, with and without the quantized state for T-S fuzzy systems, are actually superior to the existing results.</p>