This study investigates fault-tolerant H-infinity (H infinity) control for nonlinear permanent-magnet vernier generator (PMVG)-based wind turbine (WT) systems, addressing the challenges posed by signal quantization during sampling and stochastic actuator faults. To do this, the complex nonlinear dynamics of PMVG-driven WT systems are represented by linear submodels using the Takagi-Sugeno (T-S) fuzzy modeling approach, while a continuous-time, state-discrete Markov chain with multiple modes captures significant stochastic actuator failures. It is observed that existing control strategies for PMVG-based WT systems frequently neglect the combined effects of stochastic actuator faults, signal quantization, and external disturbances, which result from inherent system nonlinearities, varying wind conditions, fluctuating load demands, and other operational factors, leading to conservative designs and limited practical reliability. To address these limitations, a fault-tolerant quantized sampled-data control (FTQSDC) scheme is designed to ensure system stability, reduce communication burden, maintain physical realism under disturbances and stochastic faults, and achieve effective disturbance attenuation. Further, a novel augmented two-sided looped Lyapunov functional (TSLLF) is proposed to reduce conservatism by incorporating current state, current sampled-state, and next-step sampled-state information together with quantitative measures. Based on this framework, reliable stabilization conditions are derived via linear matrix inequalities (LMIs) to guarantee mean-square asymptotic stability of the closed-loop fuzzy WT systems, from which the corresponding controller gains are obtained. Finally, the performance of the PMVG-based WT model is simulated using practical numerical values, followed by a comparative example illustrating the superiority and advantages of the proposed theoretical outcomes.

This article deals with robust reliable H-infinity control-based stabilization criteria for offshore steel jacket platforms (OSJPs) via memory sampled-data control (SDC) with parameter uncertainty. By applying the memory SDC design into control input, a new stabilization criterion is derived that reduces the wave-induced vibration of the OSJP. The looped-functional approach is adopted to obtain sufficient conditions for the uncertain offshore platform system to resolve the stabilization of OSJP and achieve a reliable SDC design with a disturbance attenuation level gamma. Furthermore, free-matrix-based (FMB) integral inequality and linear matrix inequality techniques are used to facilitate the derivation of key results. The simulation results demonstrate the effectiveness and significance of the proposed approach.

This research aims to analyze the behavior of a Takagi–Sugeno (T–S) fuzzy system (TSFS) while considering both variable delays and packet dropouts. To achieve this, a suitable augmented Lyapunov–Krasovskii functional (LKF) is constructed, followed by establishing a new permissible and strictly dissipative condition using linear matrix inequalities (LMIs). The proposed dissipative condition ensures that the system will be asymptotically stable even in cases of random control packet dropout. The study then presents the results of numerical experiments to support the effectiveness of the proposed method. To demonstrate the practical applications of this work, the study provides three examples including a nonlinear single-species bio-economic system and the truck-trailer model. The first example also highlights the less conservativeness of this proposed method.

This paper deals with the design problem of networked cascade control systems (NCCSs) with two different event‐triggered mechanisms (ETMs) for the feedback loops. A combination of stochastic denial‐of‐service (DoS) attack and deception attack has been considered for the communication network. To comply with the practical point of view, the primary and the secondary systems have been considered with distinct disturbances. First, the closed‐loop system with state feedback controllers is provided in a unified stochastic delayed system. Then, the Lyapunov–Krasovskii stability theory and stochastic analysis techniques are employed to derive and formulate control design conditions in terms of linear matrix inequalities (LMIs). Finally, a superheater steam temperature control is utilized to assess the applicability of the proposed method.

This study addresses the problem of extended dissipativity analysis for nonlinear time-delay systems represented by Takagi-Sugeno fuzzy model. Different from the existing schemes in the literature, this paper aims to solve the dissipativity problem by considering the H-infinity, L-2 - L-infinity and dissipative performance constraints in a unified framework. Additionally, to estimate the single integral terms in the derivative of the Lyapunov-Krasovskii functional, a polynomial-based integral inequality is used. The advantage of the proposed method is demonstrated numerically with the well-known benchmark of a truck-trailler model drawn from the literature.

This paper studies the state estimation problem for memristor-based stochastic neural networks (MSNNs) with mixed variable delays.A new Lyapunov-Krasovskii functional (LKF) with quadruple integral terms is incorporated.Then, asymptotic stability conditions are established for the error system using a linear matrix inequality technique.The estimator gain can be obtained by solving the linear matrix inequalities.Numerical simulations are given to demonstrate the effectiveness and superiority of the new scheme.

This study scrutinizes the extended dissipative problem for Markovian jump generalized neural networks with asynchronous mode-dependent time-varying interval delayed states. A suitable Lyapunov–Krasovskii functional and a new bounding technique can derive delay-dependent results to achieve an extended dissipative performance index. Jensen’s inequality, reciprocally convex combination, and a novel integral inequality technique are utilized in this paper. The proposed criteria are reliable since many components are included in the unified neural network model, Markovian jumping, and time-varying delay with asynchronous modes. In this work, the systemic and time-varying delay modes are expressed asynchronously, which means that they depend on different jumping modes. Four numerical examples show the effectiveness and usefulness of the presented results.

This letter considers a memory sampled-data control for Takagi–Sugeno (T–S) fuzzy chaotic system. A memory sampled-data control technique, which includes a constant signal transmission delay stabilizes the considered T–S fuzzy system. First, a looped-functional is constructed corresponding to the chaotic system. Then, Free-Matrix-Based integral inequality is applied to solve the integral term in the derivative of the looped-functional. The sufficient conditions are obtained in terms of linear matrix inequalities (LMIs) to guarantee the asymptotic stability of T–S fuzzy systems and the existence of memory sampled-data control law. With a numerical example, the performance and importance of the proposed method are illustrated.

Reliable memory sampled-data control (MSDC) for Takagi–Sugeno (T–S) fuzzy system is considered in this study. The MSDC approach includes a signal transmission delay, is utilized to stabilize the concerned T–S fuzzy system. Unlike existing Lyapunov–Krasovskii functional (LKF) approaches, a looped functional approach is considered in this paper that minimizes conservatism in designing the desired control gains. A Free-Matrix-Based integral inequality is employed to obtain the upper bounds of the integral terms in the derivative of the looped functional. The sufficient conditions are derived to show the existence of reliable MSDC law and to assure the asymptotic stability of T–S fuzzy systems. The effectiveness and significance of the proposed approach are shown through numerical examples.

This article studies the robust exponential stability and Takagi–Sugeno (T–S) fuzzy control synthesis for networked control systems via performance. The main aim of this paper is to design fuzzy controllers with less conservative results. Novel weighted integral inequalities (WIIs) are proposed based on Wirtinger's integral inequalities. The T–S fuzzy approach expresses nonlinear systems as linear submodels with IF–THEN rules for deriving the stability conditions. Then, by constructing a suitable Lyapunov–Krasovskii functional (LKF) and applying new WIIs, improved exponential stability and stabilization criteria are established in the form of linear matrix inequalities (LMIs). Three examples are provided to demonstrate the advantages and usefulness of the proposed novel method. Two are application‐oriented examples, such as the variable speed wind turbine (VSWT) system and nonlinear mass‐spring (NMS) system.

This brief proposes a memory-based sampled-data consensus framework for general linear multi-agent systems (MAS) in the presence of a class of nonlinear actuator faults (NAF). To reduce state exchanges and preserve energy resources, communication between the neighboring agents are based on only samples of the states with variable sampling intervals. As two common constraints in the actuators, the bounded nonlinear partial loss of effectiveness and bias faults are both taken into account in the problem formulation. Sufficient conditions to guarantee consensus under the given circumstances are derived as linear matrix inequality (LMI) conditions. Different from existing Lyapunov-Krasovskii-based methods, the proposed design framework in this brief is based on a looped functional approach which reduces the conservation in designing the required consensus control gains. This less conservative approach allows a larger sampling interval as well as more severe actuator faults which together enhance the practicability of the proposed approach. Simulation results based on a tunnel diode circuit and a non-holonomic mobile robot MASs quantify the effectiveness of the proposed approach and the improved sampling intervals.

The problem of delay-range-dependent (DRD) stability analysis for continuous time Takagi–Sugeno (T–S) fuzzy time-delay systems (TDSs) is addressed in this paper. An improved DRD stability criterion is proposed in an linear matrix inequality (LMI) framework by constructing an appropriate delay-product-type (DPT) Lyapunov–Krasovskii functional (LKF) to make use of Bessel-Legendre polynomial based relaxed integral inequality. The modification in the proposed LKF along with the judicious choice of integral inequalities helps to obtain a less conservative delay upper bound for a given lower bound. The efficacy of the obtained stability conditions is validated through the solution of three numerical examples.

This paper explores an improved T-S fuzzy stabilization criteria for nonlinear systems with time-delays via memory sampled-data strategy in the looped-functional context. The sampling period is assumed to be varying within an interval. A new integral inequality is developed to minimize conservatism in the integral term. In addition, free-matrix-based integral inequality (FMBII), and Wirtinger-based integral inequality (WII) are employed. Improved stabilization conditions are derived in linear matrix inequalities (LMIs) using the FMBII, which is based on the looped-functional approach (LFA). A fuzzy memory sampled-data controller design is devised to ensure the asymptotic stability of the closed-loop system. The numerical investigation is carried out with the different approaches, and the resulting outcomes justify that the derived results are less conservative and advantageous over the existing systems.

In this paper, extended dissipative (ED) synchronization is considered for stochastic complex dynamical networks (SCDNs) with variable coupling delay via sampled-data control (SDC). First, a suitable Lyapunov–Krasovskii functional (LKF) is constructed, then a new synchronization criterion is obtained through stochastic integral inequality (SII) and linear matrix inequality (LMI) techniques. Moreover, the ED synchronization criteria are established, which consolidates passivity, dissipativity, \begin{document}$ H_\infty $\end{document}, and \begin{document}$ L_2-L_\infty $\end{document} performances in a unified structure. SDC gain matrices are also designed for each performance in ED criteria. Finally, the feasibility and usefulness of the derived theoretical results are shown through numerical simulations.

In the augmented Lyapunov–Krasovskii functional (LKF) context, this paper explores the dissipativity analysis of Takagi–Sugeno (T–S) fuzzy system with variable delays and data packet dropout. A new permissible and strictly dissipative condition in terms of linear matrix inequalities (LMIs) is derived using the higher-order Bessel–Legendre polynomial-based integral inequality (HOBLPBII), which is based on new augmented LKFs. The numerical investigation is carried out, and the resulting outcomes are given to justify the efficiency of the derived results.

In the present research, the stability analysis and stabilization of nonlinear processes characterized by the Takagi-Sugeno (T-S) fuzzy model with variable time delays have been investigated. The state's delay is presumed to belong to a given interval, ensuring that the delay lower limit is not constrained to zero. First, a new and improved integral inequality (II) lemma is proposed to deal with the cross-product terms in the derivative of the constructed delay-product-type (DPT) augmented Lyapunov-Krasovskii functional (LKF). Second, a novel delay-range-dependent (DRD) stability condition and parallel distributed compensation (PDC) technique-based stabilization condition is then achieved in terms of linear matrix inequalities (LMIs). Finally, to illustrate the superiority of the proposed stability criterion and controller design approach over existing ones, three numerical examples are given. (c) 2021 Elsevier Inc. All rights reserved.

This paper concerns the master–slave synchronization issue of neural networks subject to mixed-type communication attacks. The synchronization strategy is based on static output feedback controller followed by an event-triggered scheme. The communication network is assumed to be under various types of cyber-attacks, namely, deception, replay, and denial-of-service attacks. All these attacks are investigated in a unified Markovian jump framework. Using the Lyapunov–Krasovskii theory and stochastic analysis techniques, some design criteria are derived and formulated in terms of matrix inequalities. A convex optimization algorithm is proposed to design the static output feedback controller. Finally, two chaotic examples are presented to demonstrate the effectiveness of the event-triggered static output feedback controller.

This article addresses the investigation of strict dissipativity synchronization for a class of static neural networks under an event-triggered scheme. An event-triggered scheme is recommended, it can upgrade the exhibition of system dynamics and diminishes the network communication burden at the same time. Firstly, an appropriate Lyapunov-Krasovskii functional (LKF) with double and triple integral terms with the details on both lower and upper bounds of the delay is completely designed. Secondly, under the single and double Auxillary function-based integral inequalities (SAFBII and DAFBII, respectively) and generalized free weight matrix approach, a new class of delay-dependent adequate condition is proposed, so that the error system is (Q,S,R)−γ− strict dissipative. A resilient distributed event-triggered control scheme is developed by this criterion in terms of linear matrix inequalities (LMIs). At last, simulation examples are provided to demonstrate the performance of the derived results.

This paper studies stability and ${\mathcal{L}_2}$-gain analysis of sampled-data systems. Finsler’s lemma and free-matrix-based integral inequality (FMBII) are employed to solve the integral term in the derivative of looped functional. Based on new looped-functional, an improved stability conditions in the form of linear matrix inequalities (LMIs) are derived using the FMBII. The benefit of this technique is mainly used in reducing the conservatism and is verified based on numerical examples. Five numerical examples are given to demonstrate the validity and superiority of the proposed methods.

In this paper, a robust Stackelberg game for a class of uncertain stochastic systems with state delay is investigated. After introducing some definitions and preliminaries, we derive the conditions for the existence of the robust static output feedback (SOF) Stackelberg strategy set such that the upper bounds of leader’s cost function and the weighted cost function of the followers are minimized respectively. In order to obtain the robust SOF Stackelberg strategy set, a heuristic algorithm is proposed based on the stochastic Lyapunov type matrix equations (SLMEs) and the linear matrix inequalities (LMIs). In particular, it is shown that robust convergence is guaranteed by applying the Krasnoselskii-Mann (KM) iterative algorithm. An academic numerical example is presented to demonstrate the effectiveness of the proposed method.