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=Volume 13 - 2019

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=In this study, the stability for a class of sampled-data Takagi-Sugeno (T-S) fuzzy systems
with state quantization is investigated. Using discontinuous Lyapunov-Krasoskii functional (LKF) approach
and the free-matrix-based integral inequality bounds processing technique, a stability condition
with less conservatism is obtained, and the controller of the sampled-data T-S fuzzy system with the
quantized state is designed. Furthermore, based on the results, the sampled-data T-S fuzzy system
without the state quantization is also discussed, and the required controller is 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.