This paper addresses the problem of stabilizing aperiodic sampled-data systems using output feedback control. First, a novel two-sided looped functional is constructed, providing a sufficient condition to ensure the asymptotic stability of the resulting closed-loop system. This condition is then adapted for control design. Using a cone complementary linearization algorithm with a stringent iteration criterion, a practical approach is developed to compute the control gain. Finally, three examples, including an inverted pendulum system and a one-area load-frequency control system, demonstrate the effectiveness and superiority of the proposed method over others in the literature.

This paper presents a robust load frequency control (LFC) scheme for an islanded microgrid (IMG) based on sampled-data control. First, a novel LFC system model for an IMG is proposed by incorporating a virtual inertia control (VIC) mechanism with a sampled-data-based auxiliary controller. Then, through applying Lyapunov theory and introducing an exponential decay rate (EDR) as a performance indicator, a novel stability criterion is established for the LFC of the IMG. Based on the stability criteria, the controller design is presented by utilizingH∞performance index μ and EDR as conditions, resulting in a fast and robust LFC scheme. Finally, simulation results and comparison analysis show that the proposed method is superior, and the introduced VIC mechanism with a sampled-data-based auxiliary controller can effectively improve the dynamic performance of the system.

This article investigates the problems of exponential stability and dissipativity for neural networks with time-varying delays. To capture more information on the delay and its derivative in constructing Lyapunov-Krasovskii functionals (LKFs), the original delayed neural network (DNN) is modeled as a switching system with two modes, corresponding to cases where the delay derivative is positive or negative. This model provides extra freedom in constructing a proper LKF, allowing for the selection of different Lyapunov matrices in each mode. By applying the average dwell time (ADT) technique, several criteria for exponential stability and exponential dissipativity are obtained for DNNs. Two extensively studied benchmark examples and a quadruple-tank process control system are provided to demonstrate the superiority of the proposed criteria over some existing methods and to verify the practical applicability of the approach.

This paper investigates the stability problem of Takagi-Sugeno (T-S) fuzzy systems with time-varying delay. Different from some existing methods, a new delay-productdependent Lyapunov-Krasovkii functional (LKF) is proposed, whose derivative is estimated by using the proposed cubic function negative-determination lemma (NDL). Moreover, a parameter-dependent reciprocally convex inequality (PDRCI) is proposed to improve the estimation accuracy of reciprocally convex terms. Besides, an improved cubic function NDL is derived to solve the negative-definiteness determination of cubic functions. Based on the proposed delay-product-dependent LKF, the developed PDRCI and the improved cubic function NDL, a less conservative stability criterion is obtained. Three examples are presented to demonstrate the merits of the proposed approaches by comparing with several existing results.

This paper concerns the tracking control problem of nonlinear networked control systems (NCSs) considering external disturbance and random package dropouts. The sensor samples system states periodically. The sampled data packages may have dropouts while being transmitted through communication networks. Notice that the number of consecutive lost packages is usually lower and upper bound. Then, an aperiodically sampled data model is employed to describe such a networked control system. By utilizing an augmented state approach, the tracking control problem is transformed into the stabilization for the augmented system. A new two-sided-looped functional (TSLF) is introduced, and the system state and the sampling pattern are fully encountered. Sufficient conditions are derived to design controllers, presented as a series of linear-matrix inequalities (LMIs). A surface-mounted permanent magnet synchronous motor (SMPMSM) model and Rossler's chaotic system are provided to demonstrate the effectiveness of the proposed method.

This article concerns the stability issues of neural networks with time-varying delays. The purpose is to establish less conservative stability conditions for delayed neural networks. In order to achieve this goal, a time-varying Lyapunov functional method is proposed for stability analysis of delayed neural networks. The main feature of this method is to construct different Lyapunov functionals in different time-varying delay subintervals, which relaxes the restriction requirement of traditional Lyapunov functionals constructing a common Lyapunov functional in the whole delay interval. Based on the developed time-varying Lyapunov functional, some new stability conditions for delayed neural networks are obtained. Finally, two examples are given to verify the effectiveness of the derived stability conditions.

This article deals with the problem of stability and stabilization of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay. First, a novel Lyapunov–Krasovskii functional is constructed, which is dependent on membership functions and takes more information on the time-varying delay into account. Next, based on an$N$-order free-matrix-based integral inequality and a switching method, a cluster of criteria on the stability and stabilization are obtained for the closed-loop system connected with switching state-feedback controllers. Then, a parameter tuning method and an iterative algorithm are devised to calculate control gains. Finally, three numerical examples including the truck-trailer system are given to show that the proposed criteria can offer less conservative results than some existing ones.

This article investigates the dissipative stability and stabilization problems of Takagi–Sugeno fuzzy systems by employing sampled-data control. First, in order to enhance the adaptability of the controller, the internal and external influence factors, such as communication time delay and aperiodic sampling pattern, are taken into consideration in the design process. Then, some new terms are proposed to construct the looped-functional-like Lyapunov functional, which can improve the dissipative performance, enlarge the admissible upper bound of the aperiodic sampling periods, and reduce the occupation of control signals in the communication channel. Next, based on the free-matrix-based integral inequalities, some novel dissipative criteria are presented in terms of linear matrix inequalities. Finally, the superiority of the provided criteria is demonstrated through a truck–trailer system.

This article deals with the stability of neural networks (NNs) with time-varying delay. First, a generalized reciprocally convex inequality (RCI) is presented, providing a tight bound for reciprocally convex combinations. This inequality includes some existing ones as special case. Second, in order to cater for the use of the generalized RCI, a novel Lyapunov-Krasovskii functional (LKF) is constructed, which includes a generalized delay-product term. Third, based on the generalized RCI and the novel LKF, several stability criteria for the delayed NNs under study are put forward. Finally, two numerical examples are given to illustrate the effectiveness and advantages of the proposed stability criteria.

This article studies the stability problem of generalized neural networks (GNNs) with time-varying delay. The delay has two cases: the first case is that the delay's derivative has only upper bound, the other case has no information of its derivative or itself is not differentiable. For both two cases, we provide novel stability criteria based on novel Lyapunov-Krasovskii functionals (LKFs) and new negative definite conditions (NDCs) of matrix-valued cubic polynomials. In contrast with the existing methods, in this article, the proposed criteria do not need to introduce extra state variables, and the positive-definite constraint on the novel LKF is relaxed. Moreover, based on free-matrix-based inequality (FMBI) and new NDCs, the stability conditions are expressed as linear matrix inequalities (LMIs). Eventually, the merits and efficiency of the proposed criteria are checked through some classical numerical examples.

This article investigates the problem of delay-dependent stability for the one-area load frequency control (LFC) system with electric vehicles (EVs). Two closed-loop models of the LFC system with EVs are proposed, including the model based on the model reconstructed technique and the model with uncertain parameters that considers state of charge. By employing the Lyapunov-Krasovskii functional method, two delay-dependent stability criteria are presented for the systems under study such that a more accurate admissible delay upper bound (ADUB) can be obtained. Case studies are finally carried out to disclose the interrelationship between the ADUB, PI controller gains, and other parameters of the EVs.

This article deals with the problem of sampled-data-based synchronization of neural networks with and without considering time delay. A novel looped functional is introduced in the construction of Lyapunov functional, which adequately utilizes the state information of$e(t_{k})$,$e(t)$,$e(t_{k+1})$,$e(t_{k}-{\tau _{c}})$,$e(t-{\tau _{c}})$, and$e(t_{k+1}-{\tau _{c}})$. Then, by using this functional and employing a generalized free-matrix-based integral inequality (GFMBII), several sufficient conditions are derived to ensure that the slave system is synchronous with the master system. Also, the sampled-data controller can be obtained by using the linear matrix inequality (LMI) technique. Finally, two numerical examples are illustrated to show the validity and advantages of the proposed method.

This article is concerned with the problem of dissipativity and stability analysis for a class of neural networks (NNs) with time-varying delays. First, a new augmented Lyapunov-Krasovskii functional (LKF), including some delay-product-type terms, is proposed, in which the information on time-varying delay and system states is taken into full consideration. Second, by employing a generalized free-matrix-based inequality and its simplified version to estimate the derivative of the proposed LKF, some improved delay-dependent conditions are derived to ensure that the considered NNs are strictly ( Q , S , R )- γ -dissipative. Furthermore, the obtained results are applied to passivity and stability analysis of delayed NNs. Finally, two numerical examples and a real-world problem in the quadruple tank process are carried out to illustrate the effectiveness of the proposed method.

The passivity of neural networks with a time-varying delay and norm-bounded parameter uncertainties is investigated in this paper. A complete delay-decomposing approach is employed to construct a Lyapunov-Krasovskii functional. Then, by utilizing a segmentation technique to consider the time-varying delay and its derivative and introducing some free-weighting matrices to express the relationship between the time-varying delay and its varying interval, some improved passivity criteria are derived. Finally, two numerical examples are given to show the effectiveness and the merits of the proposed method.

This paper is concerned with the problem of stability of neural networks with time-varying delays. A novel Lyapunov-Krasovskii functional decomposing the delays in all integral terms is proposed. By exploiting all possible information and considering independent upper bounds of the delay derivative in various delay intervals, some new generalized delay-dependent stability criteria are established, which are different from the existing ones and improve upon previous results. Numerical examples are finally given to demonstrate the effectiveness and the merits of the proposed method.