A novel time-dependent sampled-data control for fuzzy Markov jump systems with cyber-attacks and missing measurements

Abstract

A time-dependent sampled-data controller (SDC) is designed to examine the stochastic stability and H∞ performance of a class of fuzzy Markov jump systems (FMJSs) subjected to limited external perturbations and time-variant mode-dependent delays. For the FMJS, first, a time-dependent SDC with randomly occurring cyber-attacks and missing data measurements that adhere to the Bernoulli distribution is developed. Subsequently, a new stochastic Lyapunov–Krasovskii functional (LKF) is developed in the mode-dependent augmented form to take full advantage of the variable properties of the actual sampling pattern. Furthermore, the principal Lyapunov term accounts for aperiodic sampling to varying degrees. To guarantee the stochastic stability of the closed-loop system, new delay-dependent criteria are established within the framework of linear matrix inequalities according to the proposed time-dependent control approach and LKF. Finally, two illustrative applications are presented to verify the mathematical findings with reduced conservatism, that is, by expanding the sampling period size and decreasing the number of decision variables.