On input-to-state stability of impulsive stochastic systems

This paper is concerned with the input-to-state stability (ISS) of impulsive stochastic systems. First, appropriate concepts of stochastic input-to-state stability (SISS) and pth moment input-to-state stability (p-ISS) for the mentioned systems are introduced. Then, we prove that impulsive stochastic systems possessing SISS-Lyapunov functions are uniformly SISS and p-ISS over a certain class of impulse sequences. As a byproduct, a criterion on the uniform global asymptotic stability in probability for the system in isolation (without inputs) is also derived. Finally, we provide a numerical example to illustrate our results.     

Robust exponential stability of uncertain impulsive stochastic neural networks with delayed impulses

In this paper, by using Lyapunov functions, Razumikhin techniques and stochastic analysis approaches, the robust exponential stability of a class of uncertain impulsive stochastic neural networks with delayed impulses is investigated. The obtained results show that delayed impulses can make contribution to the stability of system. Compared with existing results on related problems, this work improves and complements ones from some works. Two examples are discussed to illustrate the effectiveness and the advantages of the results obtained.     

Robust mean square stability of delayed stochastic generalized uncertain impulsive reaction-diffusion neural networks

This article is dedicated to the investigation of the stability problem of delayed stochastic generalized uncertain impulsive reaction-diffusion neural networks (SGUIRDNNs). Applying Lyapunov second method, several new robust mean square stability criteria on the equilibrium point of the delayed SGUIRDNNs are derived in terms of linear matrix inequalities (LMIs). At last, we provide a numerical example to verify the validity of our findings.     

Mean square exponential input-to-state stability of stochastic Markovian reaction-diffusion systems with impulsive perturbations

Mean square exponential input-to-state stability (MSEISS) is considered for stochastic Markovian reaction-diffusion systems (SMRDSs) with impulsive perturbations. Both the boundary input and distributed input are considered in SMRDSs. With the Lyapunov–Krasovskii functional method, impulse theory and inequality techniques, a sufficient condition is established to achieve the MSEISS for SMRDSs with completely known transition rate matrix. Moreover, combined with the obtained sufficient conditions, the effects of the impulse and diffusion terms on MSEISS are demonstrated by examples. Then, the case is studied that the transition rate matrix is partially unknown and sufficient conditions are presented to ensure the MSEISS in light of the introduced free constants. Finally, two numerical examples are given to illustrate the validity of our theoretical results.     

Event-triggered delayed impulsive control for nonlinear systems with applications

In this paper, we investigate the Lyapunov stability for general nonlinear systems by means of the event-triggered impulsive control (ETIC), in which the delayed impulses are greatly taken into account. On the basis of impulsive control theory, a set of Lyapunov-based sufficient conditions for uniform stability and asymptotic stability of the addressed system are obtained in the framework of event triggering, under which Zeno behavior is excluded. It is shown that our results depend on the event-triggering mechanism (ETM) and the time delays. Then the mentioned results are applied to synchronization of chaotic systems and moreover, a kind of impulsive controllers is designed in form of linear matrix inequality (LMI), where the delayed impulsive control can be activated only when events happen. In the end, to illustrate the validity of the mentioned theoretical results, we present a numerical example.     

Fixed-time stability of dynamical systems with impulsive effects

The present article is concerned with the fixed-time stability(FxTS) analysis of the nonlinear dynamical systems with impulsive effects. The novel criteria have been derived to achieve stability of the non-autonomous dynamical system in fixed-time under the effects of stabilizing and destabilizing impulses. The fixed time stability analysis due to the presence of destabilizing impulses in dynamical system, that leads to behavior of perturbing the systems’ stability, have not been addressed much in the existing literature. Therefore, two theorems are constructed here, for stabilizing and destabilizing impulses separately, to estimate the fixed-time convergence precisely by using the concept of Lyapunov functional and average impulsive interval. The theoretical derivation shows that the estimated fixed-time in this study is less conservative and more accurate as compared to the existing FxTS theorems. Further, the theoretical results are applied to the impulsive control of general neural network systems. Finally, two numerical examples are given to validate the effectiveness of the theoretical results.     

Stochastic input-to-state stability of random impulsive nonlinear systems

In this paper, the stochastic input-to-state stability is investigated for random impulsive nonlinear systems, in which impulses happen at random moments. Employing Lyapunov-based approach, sufficient conditions for the stochastic input-to-state stability are established based on the connection between the properties of system and impulsive intervals. Two classes of impulsive systems are considered: (1) the systems with single jump map; (2) the systems with multiple jump maps. Finally, some examples are provided to illustrate the effectiveness of the proposed results.     

Input-to-state stability of impulsive inertial memristive neural networks with time-varying delayed

The property of input-to-state stability (ISS) of inertial memristor-based neural networks with impulsive effects is studied. Firstly, according to the characteristics of memristor and inertial neural networks, the inertial memristor-based neural networks are built. Secondly, based on the impulsive control theory, the average impulsive interval approach, Halanay differential inequality, Lyapunov method and comparison property, some sufficient conditions ensuring ISS of the inertial memristor-based neural networks under impulsive controller are derived. In this paper, we consider two types of impulse, stabilizing impulses and destabilizing impulses. When the inertial memristor-based neural networks are originally not ISS, by choosing a suitable lower bound of the average impulsive interval, the stabilizing impulses can be used to stabilize the inertial memristor-based neural networks. On the contrary, the inertial memristor-based neural networks are originally ISS, by restricting the upper bound of the average impulsive interval, the ISS of inertial memristor-based neural networks with destabilizing impulses can be ensured. Finally, numerical results are presented to illustrate the main results.     

Practical stability of discrete-time systems

Theorems are stated and proved that provide necessary and sufficient conditions for practical stability of discrete-time systems.The first part of the paper deals with stability and instability with respect to time-varying sets, whereas the second part is devoted to the study of final and semi-final stability. The conditions obtained, which take the form of existence of discrete Lyapunov-like functions, generalize previous results.     

Stability of switching linear systems with switching signals driven by stochastic processes

This paper presents conditions to assure the exponential stability in probability for autonomous switching linear systems. The switching signal acting on the autonomous system produces intervals that follow independent, identically distributed stochastic processes—the stability then follows by verifying simple-to-check linear matrix inequalities.     

Fast finite time stability of stochastic nonlinear systems

This paper focus on fast finite time stability for a class of stochastic nonlinear systems. Firstly, a useful inequality, which will play a crucial role in the fast finite time stability in probability, is expanded on Bihari inequality. Then, the concept of fast finite time stability is discussed for stochastic nonlinear systems and an important theory concerned with fast finite time stability in probability is obtained. Next, a fast finite time state feedback controller can be subtle designed, which not only shortens the convergent time but also requires no large control effort. Three simulation examples are given to support our results of theoretical analysis.     

New stability criteria for linear impulsive systems with interval impulse-delay

The robust stability problem for linear time-delay systems with general linear delayed impulses is investigated. Different from the previous results, the impulse-delays are allowed to be larger than the impulse period. An auxiliary state variable is introduced to construct an augmented model of the impulsive system, under which the discrete dynamics introduced by impulse-delays can be highlighted. A novel piecewise Lyapunov functional is introduced to analyze the stability of the augmented model. This functional is continuous along the trajectories of the augmented model, and is not required to be positive-definite at non-impulse instants. LMI-based exponential stability conditions are derived, which depend on both the impulse-dwell-time and the impulse-delay-interval. Numerical examples show that the obtained stability criteria are able to handle the benefit/harmful impulse-delays.     

Exponential stability of non-linear neutral stochastic delay differential system with generalized delay-dependent impulsive points

This work aims to analyze the exponential stability of a non-linear impulsive neutral stochastic delay differential system. In this study, impulse perturbation is considered a delay-dependent state variable. The solution of the delay-dependent impulsive neutral stochastic delay differential system is associated with the solution of the system without impulses. First, we developed a relation connecting the solution of the neutral stochastic delay differential system without impulses and the solution of the corresponding system with impulses. Then, the conditions of the exponential stability of the proposed impulsive system are derived by determining the stability analysis of the respective system without impulse. The numerical approach for the neutral stochastic delay system without impulses is generated using the Euler-Maruyama method and adopted for the corresponding impulsive system. Finally, the achieved theoretical results are illustrated for applying the Malthusian single species neutral stochastic delay population model with immigration impulses.     

Intermittent fault detection for delayed stochastic systems over sensor networks

This paper is concerned with the intermittent fault (IF) detection problem for a class of linear discrete-time stochastic systems over sensor networks with constant time delay. By utilizing the lifting method, the distributed decoupled observers are proposed based on the output information of neighbor nodes and the node itself. In order to detect the appearing time and disappearing time of the IF, the truncated residuals are designed by introducing a sliding-time window. Furthermore, the IF detection and location thresholds are determined based on the hypothesis testing technique and the detectability of the IF is analyzed in the framework of stochastic analysis. Finally, a simulation example is presented to illustrate the effectiveness of the derived results.     

Event-triggered stabilisation for stochastic delayed differential systems with exogenous disturbances

In this paper, the practically input-to-state stabilization issue is considered for the stochastic delayed differential systems (SDDSs) with exogenous disturbances. To reduce the transmission frequency of the feedback control signal, the proposed SDDSs are stabilized by an event-triggered strategy. The concept of the practically input-to-state stability (ISS) is used to describe the dynamic performance of control target in the event-triggered schemes and exogenous disturbances. Besides, the considered SDDSs is stabilized by an event-triggered feedback controller which is represented by linear matrix inequalities. Moreover, lower bound of the interaction time of the event-triggered control method is obtained to avoid the Zeno behavior of the proposed event-triggering scheme. Finally, the effectiveness of the conclusion is verified by a numerical example.     

Mean-square integral input-to-state stability of nonlinear impulsive semi-Markov jump delay systems

In this paper, the problem of mean-square integral input-to-state stability of nonlinear impulsive semi-Markov jump delay systems is investigated. By using stochastic Lyapunov functions together with Razumikhin technique, some sufficient conditions for mean-square integral input-to-state stability for a class of nonlinear impulsive semi-Markov jump delay systems are developed. In particular, the results obtained generalize and complement some recent literature. Finally, some numerical examples are given to show the effectiveness and advantages of the proposed techniques.