This paper investigates the exponential stabilization of stochastic complex network switched systems (SCNSSs) with a novel event-triggered switching control, which comprises a primary control and a secondary control. In this control, “switching” has two implications. One is consistent with the switching signal of the system and the other refers to the switching between the primary control and the secondary control. Moreover, unlike traditional systems that utilize a single event-triggering mechanism (ETM), this paper introduces multiple ETMs to improve the control strategy by determining the updating instants of the primary control and the switching instants between the primary and secondary controls. Under the proposed novel event-triggered switching control, the asynchronous problem of time series is solved and the mean-square exponential stabilization for SCNSSs is obtained. Finally, the effectiveness of theoretical results is demonstrated through the simulation conducted on a stochastic oscillator system with multiple nodes.

In this article, we investigate the exponential stabilization of stochastic systems with sampled-data output. Two kinds of piecewise continuous functions (with/without noise) called intersample predictors are constructed to approximate discrete output signals. Corresponding predictor-based observers are then designed to obtain a good convergence of the estimation error. Based on the proposed predictors and observers, novel event-triggered strategies are developed to achieve exponential stabilization and address the asynchronism issue in input-output measurements. Finally, an illustrative example is provided to verify the effectiveness of the theoretical results.

This article discusses the problem of event-triggered controls (ETCs) for stochastic functional differential systems (SFDSs). For the stabilization of SFDSs, we design a proper degenerate Lyapunov functional and propose a novel ETC. The degenerate Lyapunov functional is more general than the traditional positive definitive Lyapunov functionals and ETC has a prominent advantage of reducing communication resources compared with periodic sampled-data feedback controls because time updating is based on a certain event. Also, a fixed positive lower bound on the stochastic interexecution times is guaranteed by this novel event-triggering mechanism. Together with ETC and the degenerate Lyapunov functional, the boundedness, mean square exponential stabilization, and globally asymptotical stabilization of SFDSs can be established for different choices of parameters. Finally, the obtained results are validated through numerical simulations.

This article is concerned with the intermittent estimator-based mixed passive and [Formula: see text] control for the high-speed train (HST) with multiple noises, actuator stochastic fault, and sensor packet loss. First, an intermittent estimator is designed to track the undetectable status of HSTs in response to only partial information available due to sensor failures. Then, two different stability criteria are developed by adopting two different Lyapunov function strategies. Simultaneously, in order to reduce the control cost and accelerate the convergence time, two different algorithms are designed. It is worth emphasizing that different from the existing results of HST subject to actuator fault, this article adopts a more flexible fault representation mode, namely, semi-Markov switching mode, which is more in line with the practical background and has a higher valuable application. Especially, the Lyapunov function designed in this article can drive the system state to decrease monotonically in both the "working interval" and the "rest interval," so as to avoid the phenomenon of state impulsive jump. Finally, through the test of HST experimental value of Japan's Shinkansen, the simulation results show the effectiveness and rationality of the proposed control method and also make a comparative analysis with related works, to prove the advantages of the control technology proposed in this article.

Throughout this article, we study the mean square exponential stabilization of stochastic retarded systems described by stochastic functional differential equations (SFDEs). Different from the traditional feedback controls with continuous observation of state, we put forward a novel sampled-data feedback control depending on the discrete observation of state and time delay. It greatly enlarges the probability of finding discrete-time feedback controls for stochastic retarded systems. In addition, the novel sampled-data feedback control succeeds in handling the delays involved in SFDEs. As applications, we provide two examples with simulation figures to illustrate the main result.