Search Results (176)

This paper studies the asymptotic synchronization problem for drive-response delayed fuzzy bidirectional associative memory (BAM) neural networks via a frequency-domain approach. The network model explicitly retains fuzzy logic operations—fuzzy AND and fuzzy OR—in its connection weights, as originally proposed in the literature. Two linear feedback controllers are designed: Controller I uses only instantaneous error feedback, while Controller II additionally incorporates a delayed error term to compensate for transmission delays. By transforming the closed-loop error dynamics into a Lur’e-type system, two novel frequency-domain synchronization criteria are derived from the multivariable circle criterion and Parseval’s theorem. The proofs are fully self-contained and detailed, using signal energy arguments to rigorously handle truncation and initial conditions without constructing Lyapunov–Krasovskii functionals. The criteria only require evaluating the minimum eigenvalue of a frequency-dependent Hermitian matrix over a bounded interval. Numerical simulations confirm the theoretical results and show that the delayed-feedback controller significantly enlarges the synchronizable region. The potential applicability of the proposed framework to associative memory, secure communication, and cooperative control is also discussed. Full article

In this paper, the uniform asymptotic stability (UAS) of nonlinear time-varying delay differential inclusions is investigated. Some new stability criteria are established from the output-to-state perspective for time-varying delay differential inclusions. Furthermore, the limiting systems associated with the time-varying delay differential inclusions are introduced to verify the weakly-zero-state-detectability (WZSD) condition. The proposed criteria can be viewed as extensions of LaSalle’s invariance principle to time-varying delay systems with multiple solutions. Finally, a tracking control example is presented to demonstrate the effectiveness of the obtained results. Full article

This paper investigates the controller design problem for switched systems with input delays. A predictor-based switched controller is proposed to compensate for the effect of input delays. By introducing an appropriate transformation, the switched system with input delays is converted into an equivalent switched partial differential equation system. The exponential stability of the resulting closed-loop system is established by constructing multiple Lyapunov–Krasovskii functionals, which allows for arbitrarily large input delays. Furthermore, the robustness of the proposed controller against perturbations in switching signals is rigorously analyzed based on the Lyapunov–Krasovskii framework. A dynamic predictor-based switched controller is also developed, and the exponential stability of the corresponding closed-loop system is guaranteed. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control schemes. Full article

The actuator-fault estimation problem is addressed for discrete-time semi-Markov jump neural networks subject to time-varying delays, external disturbances, and communication constraints induced by the weighted try-once-discard (WTOD) protocol. Under this protocol, only the measurement channel with the largest weighted error is transmitted at each sampling instant, while the unselected channels retain their previously stored measurements at the filter side. To estimate the actuator fault, a fault-weighting dynamic system is first introduced. Then, by incorporating the WTOD-induced held measurement into the state vector, an augmented estimation model is constructed to describe the fault-weighting dynamics and the protocol-induced data-holding behavior within a unified framework. Based on this model, a mode-dependent and channel-dependent reduced-order fault-estimation filter is designed. The distinctive feature of the proposed framework is that the reconstruction of selected state components and the estimation of the actuator fault are addressed within a unified reduced-order estimator whose parameters depend jointly on the semi-Markov mode and the active WTOD transmission channel. By employing a Lyapunov–Krasovskii functional and using the semi-Markov transition information together with the WTOD scheduling constraint, sufficient LMI-based conditions are derived to ensure mean-square exponential stability and strict $(T_1,T_2,T_3)-\delta$ dissipativity performance of the resulting estimation error system. Finally, two examples are provided to illustrate the numerical effectiveness of the proposed actuator-fault estimation method under different semi-Markov switching realizations. Full article

This article focuses on the projective synchronization control problem for a class of fractional-order complex-valued neural networks (FOCVNNs) with additive time-varying delays (ATVDs) subject to aperiodic denial-of-service (DoS) attacks. Initially, a switchingdelay FOCVNN model is designed to address the intermittent and unpredictable nature of aperiodic DoS attacks. Secondly, an adaptive event-triggering mechanism (AETM) with a sampled-data-based switching-adjusted threshold parameter is proposed, which can save network bandwidth effectively and strengthen the system’s defense against aperiodic DoS attacks. Third, leveraging event-triggered information, a switching controller for projective synchronization is designed to counteract the effects of DoS attacks and maintain system stability during intermittent data transmission failures. Subsequently, a novel common Lyapunov–Krasovskii function (LKF) is constructed for both the attack and dormant phases of DoS attacks, and by utilizing the fractional-order inequality method, the sufficient conditions for the projective synchronization are presented as linear matrix inequalities (LMIs). Finally, numerical examples confirm the practicality and effectiveness of the theoretical results, and demonstrate its capacity to sustain robust synchronization performance in complex attack scenarios. Full article

In this brief paper, we investigate the practical stabilization problem of multiple time-delayed positive linear time-varying systems. Firstly, a sufficient condition for the practical stability of the system is derived using the maximal separable Lyapunov–Krasovskii functional. Then, a state feedback controller is designed for the considered positive linear time-varying system with multiple time delays. Building on this method, the study further addresses the practical stabilization problem for positive linear time-varying systems with multiple state time delays and input time delays. The positivity of the linear time-varying system with input time delays is verified by designing a state feedback controller, and the practical stability is proven using the maximal separable Lyapunov–Krasovskii functional method. Finally, numerical examples are presented to validate the feasibility of the proposed method. Full article

This paper addresses the observer-based $H_{\infty }$ control problem for a class of uncertain neutral systems with distributed delays subject to norm-bounded time-varying uncertainties and bounded time-varying delays. Since complete state measurements are often unavailable in practical systems, an observer-based framework is employed to estimate inaccessible states and facilitate controller implementation. To enhance robustness against uncertainties and disturbances, an integral sliding mode control approach is adopted. By constructing an appropriate Lyapunov–Krasovskii functional that simultaneously characterizes the observer and estimation-error dynamics, novel delay-dependent stability conditions are established in terms of linear matrix inequalities (LMIs). Based on these conditions, a sliding mode control law is developed to ensure the reachability of the prescribed sliding surface, while an observer-based controller is designed to guarantee the robust asymptotic stability of the resulting closed-loop system and achieve a prescribed $H_{\infty }$ performance level. Finally, numerical examples are presented to illustrate the effects of the distributed delays on the considered system and to validate the effectiveness and applicability of the proposed method. Moreover, the proposed results are shown to reduce conservatism and improve computational efficiency compared with existing results reported in the literature. Full article

Inverter-dominated microgrids are highly vulnerable to stealthy cyber–physical drift attacks, low-amplitude, slowly varying perturbations that bypass conventional statistical filters to induce voltage degradation and delayed collapse. This paper introduces a resilient, delay-aware supervisory control architecture that acts as an online safety shield at the actuator interface. By jointly modeling nonlinear power-flow interactions and directional communication topologies, we construct physics-informed Control Barrier Functions (CBFs), embedding structural electrical invariants derived from the nodal admittance matrix $Y_{bus}$. The supervisor directly incorporates heterogeneous, time-varying network delays into its safety constraints and utilizes a threat-adaptive modulation loop driven by spatio-temporal residuals to dynamically scale intervention aggressiveness. Using a Lyapunov–Krasovskii functional, we prove that the closed-loop tracking error is Input-to-State Stable (ISS) under bounded drift and worst-case latencies. High-fidelity simulations on an IEEE 14-bus test feeder demonstrate that the supervisor consistently enforces non-negative safety margins and reduces time-integrated voltage violations. Under coordinated sub-threshold attacks designed to exploit network jitter, the architecture bounds trajectories to physically consistent manifolds and prevents voltage collapse, establishing a scalable cross-layer safety framework for resilient distribution systems. Full article

This paper investigates an observer-based secure control method for networked non-Lipschitz nonlinear systems subject to unknown nonlinearities, external disturbances, sensor noises, and intermittent denial-of-service (DoS) attacks. Multi-layer neural networks (MNNs) are adopted to compensate for non-smooth, non-Lipschitz terms, guaranteeing bounded approximation errors. A resilient high-gain observer fused with the MNN is developed to continuously reconstruct system states. When DoS attacks block sensor channels, the observer acts as a virtual dynamic engine to substitute for lost real-time measurements, providing uninterrupted feedback to the controller. Furthermore, to optimize communication efficiency, an observer-based static event-triggered mechanism (SETM) coupled with a hold-input strategy is integrated. Employing the Lyapunov–Krasovskii functional method, sufficient conditions are derived to prove that the closed-loop system remains uniformly ultimately bounded (UUB) under the joint effects of approximation errors, disturbances, and attacks. Simulation results on a two-link manipulator demonstrate that the proposed secure control scheme effectively counters aggressive DoS attacks while achieving a 56.8% reduction in network transmissions compared with conventional periodic sampling paradigms, striking a favorable balance between tracking accuracy and resource efficiency. Full article

Existing approaches for admissibility analysis of T-S fuzzy descriptor time delay systems fail to balance conservatism reduction and computational complexity. This paper proposes a low-conservatism analysis and stabilization method based on the symmetric Lyapunov–Krasovskii (L-K) functional. By exploiting the boundedness of membership function derivatives, and combining Jensen’s integral inequality with auxiliary slack matrices to achieve tight bounding of nonlinear terms, we derive an admissibility criterion for open-loop systems with significantly reduced conservatism. A well-suited L-K functional is constructed targeting the structural characteristics of fuzzy singular matrices $E_{\xi }$, a state feedback controller is designed via the parallel distributed compensation (PDC) strategy, and solvable sufficient conditions for the admissibility of closed-loop systems are established. Numerical examples demonstrate that the maximum allowable delay upper bound obtained by the proposed method outperforms that of existing state-of-the-art approaches while balancing conservatism and computation cost and verifying the superiority of the proposed method. Full article

This paper investigates the problems of anti-synchronization for a class of inertial neural networks (INNs) with time-varying delays under the influence of deception attacks and hybrid triggered control. A novel dynamic hybrid-triggered control (DHTC) scheme is developed to utilize communication resources and enhance network security efficiently for the model INNs. By integrating the extended dissipative approach with Lyapunov–Krasovskii functional (LKF) techniques, new sufficient conditions are established to ensure the quadratic stability of the resulting closed-loop system. The proposed framework not only unifies the anti-synchronization problems but also extends classical passivity, (Q, S, R)-dissipative, $H_{\infty }$, and $L_2-L_{\infty }$ results as special cases. Moreover, the DHTC mechanism dynamically switches between time-triggered and event-triggered modes, reducing unnecessary signal transmissions while maintaining system stability against deception attacks. Finally, simulation results on delayed INNs demonstrate the effectiveness and superiority of the proposed theoretical and control strategy. Full article

Time-delay systems (TDSs) arise in many engineering applications where sensing, actuation, computation, transport, or communication delays affect closed-loop stability and performance. Classical control methods, including predictor-based control, Lyapunov–Krasovskii functional approaches, robust control, model predictive control, and adaptive control, provide rigorous theoretical foundations for delay compensation and stability analysis. However, their effectiveness may be limited when the system is nonlinear, uncertain, poorly modeled, or subject to unknown and time-varying delays. In recent years, artificial intelligence (AI)-based methods, such as neural networks, fuzzy systems, deep learning, and reinforcement learning, have attracted increasing attention for their capabilities in learning, approximation, prediction, and adaptation. This paper presents a comprehensive review and bibliometric analysis of control strategies for TDSs, with an emphasis on the interactions among classical, AI-based, and hybrid methods. Publications indexed in the Web of Science database from 2010 to 2025 are analyzed using bibliometrix and VOSviewer to identify publication trends, influential contributors, collaboration patterns, citation structures, and thematic evolution. In addition, a unified framework is proposed to classify TDS control strategies into classical, AI-based, and hybrid categories. The results show that classical stability and robustness analysis remain central to the field, while AI-based and hybrid methods are increasingly used to address nonlinearities, uncertainties, communication delays, and real-time implementation challenges. Finally, key research gaps and future directions are discussed, including stability-guaranteed learning, learning-based delay compensation, interpretable AI control, benchmarking, and practical deployment in cyber-physical, robotic, aerospace, and networked systems. Full article

This paper investigates the problem of stability analysis for Takagi–Sugeno fuzzy systems with time-varying delays. By integrating an augmented delay-dependent Lyapunov–Krasovskii functional (LKF) structure, a refined LKF based on auxiliary function-based integral inequalities, and utilizing a linear switching method, this paper proposes less conservative stability criteria that effectively enhance fuzzy membership characteristics. The proposed stability criteria are formulated in the framework of linear matrix inequalities. Through three numerical examples, the effectiveness and superiority of the proposed approach are demonstrated by achieving significantly improved maximum delay bounds compared to the existing literature. Full article

This paper investigates the leader–follower consensus problem for fractional-order nonlinear multi-agent systems operating under simultaneous Sybil and Denial-of-Service (DoS) attacks. The communication topology is modeled as a time-varying directed graph with intermittent link failures due to DoS disruptions, while malicious data injection induced by Sybil attacks is incorporated into the agent dynamics. In addition, bounded disturbances and time-varying input delays are explicitly considered. To counter these challenges, an event-triggered distributed control framework was developed to reduce communication load while preserving agents’ tracking performance. Furthermore, an adaptive compensation mechanism is introduced to estimate and attenuate the combined effects of cyber attacks and external disturbances. A novel Wirtinger-type fractional integral inequality is established, providing a less conservative tool for constructing Lyapunov–Krasovskii functionals in fractional-order systems. Sufficient conditions for asymptotic leader–follower consensus are obtained in terms of linear matrix inequalities using fractional Lyapunov stability theory. The proposed scheme guarantees the convergence of tracking errors, excludes Zeno behavior through a decaying triggering threshold, and ensures robustness against malicious signal injection and communication interruptions. The results demonstrate that the developed event-triggered adaptive strategy achieves resilient consensus in fractional-order multi-agent systems despite simultaneous cyber attacks at both the network and information layers. Full article

This paper investigates dynamic event-triggered control (DETC) and encoding–decoding schemes to achieve the synchronization of T-S fuzzy neural networks (FNNs). DETC allows the transmission signals to be controlled aperiodically during the actual operation of the system, enabling a rapid response to practical control tasks. Meanwhile, during the event-triggered control process, an encoding–decoding scheme with externally injected noise is used to protect the signals. First, a dynamic event-triggered control mechanism is established, and an encoding–decoding scheme is used to optimize the transmission of controller signals. Subsequently, the Lyapunov–Krasovskii functional is constructed to derive the system’s synchronization criteria and calculate the controller gains. Finally, numerical simulation experiments are conducted to verify the effectiveness and feasibility of the proposed method. Full article