This article is dedicated to studying the discrete time output sampling stabilization problem of T–S fuzzy (TSF) time-varying delay systems with stochastic disturbance and actuator saturation. Different from the existing sampling control that is converted to input time-delay type, the fuzzy sampling in this article is output sampling feedback control at discrete time points. More importantly, the proposed method eliminates the limitation of previous results on the relation between the magnitude of the time delay and the upper/lower bound of the sampling interval. Especially, based on the provided two different analysis methods of the multipiecewise time-dependent Lyapunov–Krasovskii functional and the multisegment time-dependent Lyapunov–Razumikhin function, which can effectively capture more dynamic characteristics of the target system and greatly reduce the conservatism of the relevant results. Finally, two practical models are provided to demonstrate the rationality and superiority of the developed approach.

The future extensive usage of continuous measurable communication network for output feedback control in unmanned surface vehicle (USV) will inevitably increase the control cost, waste energy, and even reduce the system performance under stochastic cyber attacks. However, the existing study largely ignores the guaranteed cost control of USV under stochastic cyber attacks. To fill the knowledge gap, this paper attempts to focus on the guarantee cost control of USV systems by taking into account the finite measurable output sampling information and stochastic cyber attacks. Initially, a point output sampling control strategy based on finite measurable output sampling information is proposed, which has a strong application background and important theoretical research value in the field of power grid security. On this basis, a new stochastic composite system containing both system state and output sampling information is developed. Subsequently, a sampling-time-dependent Lyapunov function (LYFU) is exploited to ensure the workable criteria of the cost sampling controller, which can not only stabilize the stochastic composite system by means of the mean square exponential stability, but also reveal the upper bound (UPBO) of the quadratic cost function (COFU). Specially, in the strict sensu of minimizing the UPBO of the COFU, a meaningful design method for a suboptimal guaranteed cost output sampling controller is designed, which is a modulus-related LMI optimization algorithm. Finally, numerical simulation results of USV and additional vehicle lateral motion model are developed to demonstrate the validity and rationality of the proposed design method.

This paper focuses on the fuzzy model-based quantitative control for prefixed-time synchronization of reaction-diffusion (RD) complex networks under stochastic noise, cyber-attacks and saturation. Different from the existing finite/ fixed time synchronization, a concept of prefixed-time synchronization is proposed to cope with cyber-attacks and stochastic noise in the target system, rather than the common finite/fixed-time synchronization. Then, two different prefixed-time synchronization criteria are presented by designing two appropriate quantization controllers and adopting the well-known probability-density inequality and the generalized sector condition techniques. Note that the proposed quantization controller can effectively overcome the constraints of communication channel and bandwidth limitation caused by cyber-attacks. Especially, the given quantization controller does not adopt the common sign function so as to avoid the controller’s quivering behavior, which effectively reduces the conservatism. Finally, two simulation results are provided to verify the rationality and superiority of the developed control design scheme in this paper. Note to Practitioners —It is an indisputable fact that dynamic behaviors based on complex networks sometimes depend not only on their temporal information but also on spatial locations to a large extent, such as chemical and biological processes. Therefore, the research in this study is the complex networks with RD terms rather than the common complex networks of ordinary differential form. In addition, cyber-attacks, stochastic disturbance, actuator saturation, etc. exist widely in network systems, which are completely unavoidable. Therefore, when complex networks with reaction-diffusion terms are also affected by these factors, can they achieve synchronization? In particular, is existing fixed/finite time synchronization strategy still feasible when complex networks are suffer from stochastic disturbances rather than ordinary unknown/known bounded disturbances? These questions puzzled the authors and prompted this study.

This paper mainly focuses on the problem of non-fragile intermittent estimate tracking in microgrid systems with denial of service (DoS) attacks and gain fluctuation. Different from the common DC/AC microgrid system, the microgrid system studied in this paper involves large grid control and power line communication technology, which is more in line with the actual operating environment. Simultaneously, aiming at the occurrence of network-induced DoS attacks, an intermittent estimator based on partial measure information under cyber-attacks is designed. More importantly, instead of the existing single aperiodic or periodic DoS attacks, the DoS attacks scheme presented in this paper is semi-periodic DoS attacks whose the attack range is aperiodic and the attack width is certain. Furthermore, a variable obeying the Bernoulli distribution is proposed to characterize the randomly occurring gain fluctuation, in which the no-fragile observation is stochastic uncertain. Then, two sufficient conditions of stochastic stability of observation error system of microgrid are devised with the assistance of two different Lyapunov theories. Finally, the rationality of the developed theoretical method is verified by a numerical example.

In this paper, some unified stochastic finite-time stability (SFTS) criteria are derived for the impulsive stochastic nonlinear systems by using the stochastic analysis theory, stopping time technology, Lyapunov approach and limit average dwell time (LADT) technology when the impulse effect is destabilizing impulse or stabilizing impulse. Meanwhile, in order to achieve stochastic finite-time stabilization for stochastic nonlinear systems, the impulse control approach is also obtained, which can be divided into two cases: (1) A stochastic finite-time instable (SFTI) stochastic system can become SFTS under the impulse control strategy; (2) A SFTS stochastic system can keep the SFTS under the impulsive disturbance. Furthermore, by using the impulse dependent average dwell time (IDADT) technology, the results are also established for the multiple impulse effects, i.e. destabilizing impulse and stabilizing impulse exist in the system simultaneously. Finally, two useful examples are provided.

This paper is concerned with the finite time $H_{\infty} $ composite anti-disturbance control problem for Markov switched descriptor systems with multiple disturbances and packet loss via a disturbance observer. Significantly, the switching topology of descriptor systems is controlled by nonhomogeneous Markov switching processes, whose time-varying transition probability is limited by a convex hull. Subsequently, a Bernoulli random variable is exploited to characterize the intermittent measurement mode behavior of controller-actuator packet loss. Furthermore, the stochastic $H_{\infty} $ finite time boundiness of the composite system and simultaneously suppression and rejection of external disturbances are established by resorting to disturbance observer-based robust control (DOBC) strategy and a new stochastic Lyapunov function technique. More importantly, a relaxed variable method is provided to eliminate the coupling between Lyapunov variables and the system matrix in the process of stability analysis, instead of eliminating the coupling by means of commonly-used traditional inequalities, which effectively increases the flexibility of the obtained stable results and greatly reduces the computational complexity of controller/observer in the existing works. Finally, the effectiveness and practicability of the developed results are verified by two practical engineering models.

This article is concerned with the $\mathscr {L}_{1}$ -stabilization of switched positive systems with stochastic interval delay by using an intermittent static output feedback control strategy. Different from previous results, this article mainly focuses on the system state, which is not only completely measurable due to external disturbance and sensor fault, rather than the result of continuous and complete measurement of the system, which is more in line with the actual system situation, but it also yields much difficulty. To overcome the difficulty, a novel intermittent static output feedback control method is proposed in this article, which is also the first time to be applied to the class modification system. More importantly, unlike the existing switched positive system, which only considers deterministic time delay, the time delay considered in this article occurs randomly in the interval, which has more research significance and value. To verify the correctness of the developed strategy, a practical model is provided.

This brief mainly investigates the stabilization problem for a class of state-dependent switched reaction-diffusion neural networks (RDNNs) with stochastic disturbance and impulsive effects. First, a hybrid control strategy of time domain intermittent-spatial domain point sampling is proposed, which can greatly reduce the control cost. Then, by using the constructed multi-partition Lyapunov function and the convex combination method, two different stabilization criteria are derived for the target system with stabilizing impulse and destabilizing impulse, which are shown in the form of easy-to-calculate LMIs. Moreover, the multi-gain phenomenon is effectively avoided through the single decision-multivariable technique, which facilitates the operation of the actual system. Note that our results are more practical than those established only on pure stabilizing impulse and destabilizing impulse. Particularly, one of the results shows that even if the switched RDNN with destabilizing impulse is unstable, it can be stabilized by the hybrid intermittent sampling control strategy proposed in this brief. Finally, numerical examples show the rationality and validity of the new results.

This paper focuses on the stochastic passivity problem of stochastic memristor-based complex valued neural networks with two different types of time-delays and reaction-diffusion terms by sampled-data control strategy. Different from the existing sampled-data strategies, this paper develops spatial and temporal point sampling, namely, only a finite number of points in space or time are sampled. By introducing two different Lyapunov functional and employing techniques such as Wirtinger’s integral inequality, Jensen’s inequality and Young’s inequality, etc., two different sufficient conditions for the stochastic passivity of the system are established. Prominently, the condition quantitatively reveals the relationship between the upper and lower bounds of the sampling interval at spatial and temporal points. Finally, a numerical example is given to verify the rationality of the proposed method. Notice, compared with a large number of results of real-valued reaction-diffusion neural networks, the research results of sampled-data controlled complex-valued reaction-diffusion neural networks have not appeared so far, and this work is the first attempt to fill in the gaps in this topic.

This article focuses on the problem of prefixed-time synchronization for stochastic multicoupled delay dynamic networks with reaction-diffusion terms and discontinuous activation by means of local intermittent sampling control. Notably, unlike the existing common fixed-time synchronization, this article puts forward a new synchronization concept, prefixed-time synchronization, based on the fact that stochastic noise and discontinuous activation can be seen everywhere in practical engineering, which can effectively perfect and improve the existing works. Specifically, a local intermittent in the time domain and point sampling control strategy in the spatial domain is proposed instead of a simple single intermittent control approach, which greatly reduces the control cost. In addition, by some effective means, including the famous Young's inequality, Jensen's inequality, and Hölder's inequality, we obtain two different synchronization criteria of the networks without delay and with multicoupling delays and deeply reveal the quantitative relationship among control period, point sampling length, and network scale. Finally, a numerical example is given to verify the effectiveness of the developed method and the practicability by Chua's circuit model.

In this paper, a (Q, S, R)-(r) over tilde -dissipative control rate based on intermittent observations is developed for the Korteweg-de Vries-Burgers equation (KdVB) with stochastic noise and incomplete measurable information. Different from the existing works, the measurement transmissions from the sensor to the controller and from the controller to the actuator are assumed to be imperfect (i.e., the phenomenon of data packet dropouts may occur intermittently), and an intermittent observer is proposed to track the part state of the equation rather than the existing traditional full-order Lebesgue observer. In addition, a (Q, S, R)-(r) over tilde -saturation dissipation index is selected to design a unified control for KdVB dynamics, which is more general than H-infinity exponent. More importantly, the calculated (Q, S, R)-(r) over tilde -saturation dissipation performance is completed under the condition of partial state loss of KdVB, which has great research significance and value in practical engineering applications. Finally, an example is given to verify the effectiveness and superiority of the proposed method. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

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.

This paper explores the switching synchronization problem of reaction-diffusion neural networks with time-varying delays, and two improved synchronization switching law strategies are proposed for stability analysis. One is constructed by adopting a Lyapunov-Krasovskii functional combined with the use of improved Wirtinger's integral inequality for managing the reaction-diffusion terms. The other is designed to utilize the Lyapunov-Razumikhin function, which is easier to deal with the reaction-diffusion terms directly compared to the former one. As a result, the time-space feature of the proposed switching synchronization is more robust and compatible than previous works. Finally, the simulated numerical experiments make out the effectiveness of the developed approaches in this work.

In this article, the extended dissipative performance of distributed parameter systems (DPSs) with stochastic disturbances and multiple time-varying delays is studied by using a new fuzzy aperiodic intermittent sampled-data control strategy. Different from the previous fuzzy sampled-data control results, the state sampling of the proposed sampled-data controller occurs only in space and is intermittent rather than continuous in the time domain. By introducing a novel multitime-delay-dependent switched Lyapunov functional to explore the dynamic characteristics of the controlled system, and by means of the famous Jensen’s inequality with reciprocally convex approach, Wirtinger’s inequality, the criterion of the system’s mean square stabilization is established based on the LMI technique, which quantitatively reveals the relationship between the control period, the control length, and the upper bound of the control sampling interval. Especially, the optimal control gain is given by designing an optimized algorithm in the article, which greatly reduces the cost. Finally, two numerical examples are presented to demonstrate the effectiveness and superiority of the proposed approach.

This paper is devoted to investigating the issues on extended dissipative anti-disturbance control for switched singular semi-Markovian jump systems with multiple disturbance and time-delay via disturbance observer. Different from the existing results, this paper introduces a relatively new and comprehensive system model, namely, switched semi-Markovian jump model, which is more general than Markovian jump model in describing the phenomenon of random mutation of system structure and parameters. Moreover, the uncertain transition rates in the considered switched semi-Markovian process include two cases of indeterminate bounded and thoroughly unknown, rather than the common single partially unknown case. Particularly, the designed composite controller that ensures that the closed-loop system achieves stochastic admissible is easier to calculate than the related working methods. In addition, an optimization algorithm to constrain control gain is developed in this paper, which can effectively reduce control cost in practice to some extent. Finally, for the sake of substantiating the validity of the proposed method, a numerical example is provided.

This paper addresses the problem of uncertain fuzzy intermittent extended dissipative control to stabilize the flexible spacecraft (FS). However, to the systems with actuator random failures, input saturation and Bernoulli stochastic distribution by utilizing a novel switching Lyapunov function method. Compared with the existing common Lyapuonv function, the proposed approach is convenient for us to exploit more information of switching interval. Especially, the condition that control length and control period must be a certain proportion in the previous works of intermittent control strategy is removed in this work, which promotes the obtained criterion to be less conservative. Numerical simulations are finally given to demonstrate the validity and superiority of the theoretical results.

This brief is concerned with the drive-response synchronization problem of memristor networks subject to actuator failures and two different activations via sampled-data (SD) control strategy. By using 8-matrix measure method and Halanay inequality technique, the sufficient conditions for global exponential synchronization of driving-response memristor systems are obtained. Unlike the existing works, the common Lyapunov functionals are not employed, but two simple and general stability criteria are derived. Particularly, the established criteria are easier to demonstrate and execute in practice than the relevant results. And in order to verify the validity and superiority of the proposed theoretical results, a numerical simulation is finally provided.

This paper is devoted to investigate the issue of fault-tolerant sampled-data control for a class of uncertain fractional-order memristive neural networks with random switching topologies subject to stochastic sensor faults via impulsive method. Firstly, a fault detection sampled-data control strategy is proposed for sensor failure. Then, by utilizing the constructed quasi-periodic polynomial Lyapunov function, the criterion for ensuring that the transformed pulse system achieves asymptotic stability is established. Moreover, based on this criterion and with the help of the mechanism of sum of squares (SSs), the desired reliable control gain matrix is obtained. Finally, a numerical example is employed to demonstrate the validity of our method, and this method is applied to the well-known fractional-order Chua’s circuit system. Compared to large quantities of results for integer-order memristive neural networks, only a small number of results are for fractional order. In particular, the sampling-controlled fractional-order memristive neural networks have not been solved until now, let alone the novel impulive approach designed in this paper. This paper is the first attempt to make up for this vacancy in this topic.

This article focuses on the intermittent nonfragile control problem for a class of stochastic neutral-type time-varying delay systems with randomly occurring uncertainties. First, the problem is transformed into the intermittent stabilization problem of generalized systems by variable substitution. Second, based on the dynamic characteristics of the intermittent controlled generalized systems, the time-dependent switching Lyapunov functional is introduced, and the convex combination technique is applied to provide sufficient conditions for the stability of the systems. Then, it is proved that the conditions can be transformed into a feasible solution of a group of linear matrix inequalities. Finally, several numerical examples are provided to verify the effectiveness and advantages of the proposed method.

This paper mainly studies the problem of extended dissipativity stabilization and synchronization of a class of uncertain stochastic reaction-diffusion neural networks with discrete and distributed time-varying delays via intermittent non-fragile control strategies. A novel stabilization and synchronization criteria and extended dissipative analysis is obtained by combining a switching time dependent Lyapunov functional method with Wirtinger’s inequality technique. Then, by solving a set of delay dependent linear matrix inequalities, we obtain the desired intermittent non-fragile controller, which can be used to satisfies the prescribed level of extended dissipativity behaviors, including H∞ behavior, passivity action, (Q, S, R)-dissipative performance, and L2−L∞ performance. Finally, two numerical examples are adopted to verify the validity of the stabilization and synchronization results.