This paper is concerned with reliable robust sampled-data control for T–S fuzzy semi-Markov jump systems (SMJSs) with time-varying delay. The asynchronous model is established, which makes the fuzzy sampled-data controller share the premise variable of the fuzzy plant with the fuzzy system. By adjusting the free weight matrix in the concept of extended dissipative, $H_{\infty }$, $L_2-L_{\infty }$, passive and $\left(Q,S,R\right)$-dissipative performance are solved in a unified framework. A novel mode-dependent Lyapunov–Krasovskii functional (LKF) is constructed, which fully utilizes the characteristics of the real sampled period. Based on Lyapunov stability theory, Newton–Leibniz condition and new integral inequality techniques, some less conservative sufficient conditions are obtained to guarantee the close-loop system is stochastically stable and extended dissipative. Based on sampled-data approach, a robust reliable controller can be developed by solving the linear matrix inequalities (LMIs). The advantage and effectiveness of the proposed design method can be illustrated by the numerical example.

本文涉及具有时变时滞的TS模糊半马尔可夫跳跃系统（SMJS）的可靠鲁棒采样数据控制。建立了异步模型，使模糊采样数据控制器与模糊系统共享模糊工厂的前提变量。通过在扩展耗散的概念中调整自由重量矩阵，$H_{\infty }$， ${\mathrm{大号}}_2-{\mathrm{大号}}_{\infty }$，被动和 $\left(问，\mathrm{小号}，\mathrm{[R}\right)$耗散性能在统一框架中解决。构造了一个新颖的依赖于模式的李雅普诺夫–克拉索夫斯基函数（LKF），它充分利用了实际采样周期的特征。基于李雅普诺夫稳定性理论，牛顿-莱布尼兹条件和新的积分不等式技术，获得了一些保守程度较低的充分条件，以确保闭环系统具有随机稳定性和耗散性。基于采样数据方法，可以通过解决线性矩阵不等式（LMI）来开发鲁棒的可靠控制器。数值实例可以说明所提出的设计方法的优点和有效性。