This paper studies the static output feedback control issue for discrete-time singularly perturbed switched time-delay systems subject to randomly occurring deception attacks, in which the sequence for activated subsystems is governed by the persistent dwell-time switching strategy. The practical present phenomena that parameters alternately switch in fast or slow frequencies are characterized suitably with such strategy. Meanwhile, the deception attacks are assumed to occur in a random way that obeys the Bernoulli distribution. Moreover, some sufficient conditions are established by means of the Lyapunov stability theory, which guarantee the closed-loop system is mean-square exponentially stable with a prescribed ∞ performance. Furthermore, based on the matrix processing technology, the desired controller gains are obtained. Finally, the rationality and effectiveness of the proposed method are verified by a numerical example.
This paper is concentrated on the H∞ state estimation problem for switched coupled neural networks based on a Takagi-Sugeno fuzzy model. Notably, the time-variant network topology with alternate fast switchings and slow ones is described suitably by a persistent dwell-time rule, and the interactive dynamics with both cooperative properties and antagonistic ones among nodes are featured comprehensively by the switching signed graph. In view of the communication pressures brought by network-induced problems and the requirements in digital control, the round-robin protocol and logarithmic quantization are flexibly integrated for more transmission efficiency and fewer data collisions. Thereafter, by utilizing a relaxed multiple Lyapunov function method and some novel matrix process techniques, sufficient criteria guaranteeing the exponential stability in a globally uniform sense with a prescribed H∞ performance level of the estimation error system are established. Finally, the synthesized analysis of the proposed method is presented with an illustrative example.
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