Quantized Sampled-Data Control for T-S Fuzzy System Using Discontinuous LKF Approach - Wang, Shenquan; Chen, Shuaiqi; Ji, Wenchengyu; Liu, Keping (Frontiers in Neuroscience, 2019)
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.