This work addresses the secondary control issues in microgrid for frequency and voltage restoration under network threats. Firstly, a distributed control protocol is constructed using the consensus method of multi-agent system (MAS). Secondly, sliding mode control (SMC) using an improved stretchy event-triggered mechanism (ISETM) is designed to reduce data transmission. By employing Acknowledgment (ACK) mechanisms, ACK-based self-recovery (ASR) strategy is developed as an attack detection and compensation scheme to improve control performance under unsecured communication environment. Then, sufficient conditions are derived using Lyapunov stability theory to ensure a finite time convergence on the sliding mode surface. Finally, a test system consisting of 20 DGs is applied to assess the scalability of this distributed control algorithm. Experiment tests on a hardware-in-the-loop (HIL) platform are conducted to verify that the designed control scheme offers advantage and efficiency on microgrid secondary control.

The open radio access network (RAN) technology is crucial to the realization of autonomous driving, however, it also leads to the emergence of privacy protection and network security issues. This article explores the privacy-preserving consensus tracking problem of connected vehicular group systems over open RANs subject to stochastic false data injection (FDI) attacks. First, based on the polytope linearization technique and graph theory, a distributed connected vehicle group system model with multiple uncertain parameters is established under the two-layer cyber-physical framework. Then, by resorting to the homomorphic encryption method, a novel privacy-preserving consensus control strategy is designed via a vanishing affine output mask function, which not only prevents the leakage of initial state information but also improves the resilience of vehicle state to FDI attacks. Furthermore, an intelligent event-triggered network transmission scheme is proposed by introducing the Q-learning algorithm to dynamically adjust the triggering threshold during each iteration, which greatly lightens the communication load of open RANs. Finally, an illustrative example is presented to verify the validity of the derived theoretical results.

The deceptive nature of false data injection (FDI) attacks and the hysteresis effect of asynchronous leakage delay (ALD) can weaken the resilient load frequency control (RLFC) system’s dynamic response and stability. Therefore, this paper investigates the system dynamic fluctuations caused by the coupling of FDI and ALD. First, a dynamic attack model is established. An attack modulator (AM) is introduced to dynamically adjust the attack process to accurately simulate attacks on critical control paths. Second, we systematically characterize the asynchronous leakage delay caused by multi-source communication link switching. This delay behavior is embedded into the control system. This enables the system to more comprehensively reflect feedback delay characteristics under complex communication environments. Next, a robust control method is designed to enhance the system’s response to FDI and ALD under these complex situations, which can effectively ensure the stability of the system. Then, the improved Lyapunov-Krasovskii functional is used to analyze the system performance. Finally, simulations of a dual-region power system are conducted. The simulation results verify the effectiveness of the proposed method in terms of disturbance immunity, control stability, and engineering adaptability.

This paper investigates the resilient synchronization problem of multi-agent systems (MASs) with multiple leaders under random link failure constraints (RLFCs). Firstly, in order to overcome the vulnerability on communication links of single-leader MASs, a model of MAS with multiple leaders under RLFCs is established. Secondly, to address the design problem of resilient control, a distributed leader state observer is introduced. Then, the distributed leader state observer with adaptive capability is designed to resolve the unpredictable states of the leaders under RLFCs. Thirdly, to obtain a reliable PI controller with high performance, stochastic stability criteria are derived via the algebraic Riccati equation and an appropriate Lyapunov-Krasovskii function (LKF). Finally, a simulation based on actual F-16 flight results is given to demonstrate the feasibility of the proposed control strategy.

In this article, we address the error estimation problem of quasi-synchronization for a class of multilayer dynamical networks. The proposed network model simultaneously accounts for interlayer and intralayer time-varying coupling structures, network directionality, and interlayer communication delays. To achieve synchronization in a cost-effective manner, we design a novel pinning impulsive control strategy that leverages large-scale impulse delay information together with the number of pinned nodes. By employing an iterative algorithm, we establish a new delay-dependent impulsive differential inequality, which precisely characterizes the convergence domain and provides flexibility in the choice of impulse delays. Then, some quasi-synchronization criteria are derived to guarantee convergence of multilayer networks within a prescribed error level, and explicit analytical expressions for the synchronization error bounds are obtained. Finally, to demonstrate the practical applicability, the proposed criteria are applied to the synchronization of multilayer single-link robot arm networks under error bounds, with numerical examples validating the effectiveness of the method.

Output tracking control has been extensively applied in the cooperative control of multiagent systems (MASs), including mobile robot obstacle avoidance and autonomous aerial vehicle formation. This article investigates the $H_{\infty }$ output consensus tracking problem of interval type-2 (IT2) fuzzy heterogeneous MASs subject to multiple-channel denial-of-service (DoS) attacks. First, in the presence of DoS attacks on both leader-follower and follower-follower communication channels, a fully distributed adaptive compensator is designed to approximate the convex hull of the leader's state. Then, to address DoS attacks occurring in the compensator-controller and observer-controller channels, an observer-based fuzzy switched controller is developed for each follower agent to accomplish the tracking objective. Furthermore, a two-layer hierarchical hybrid event-triggered mechanism (ETM) is established to significantly reduce the communication burden of the MAS network. In the proposed ETM, asynchronous communication and Zeno behavior are rigorously excluded, while the triggering frequency is effectively decreased. Moreover, a sufficient condition is derived to guarantee the exponential stability of the tracking error with a prescribed $H_{\infty }$ performance. Finally, simulation results are provided to demonstrate the feasibility and superiority of the proposed approach.

This article addresses the time-varying distributed optimization problem (DOP) for networked multiagent systems (NMASs) operating over directed graphs, considering the impact of edge-based additive measurement noise (EBAMN). First, a finite-time stochastic stability framework is established to demonstrate the global stochastic practical finite-time attraction of the origin, enabling robust control design for stochastic nonlinear systems. The proposed method achieves faster convergence rates and provides bounded finite convergence time estimates, outperforming asymptotic methods. Second, a novel distributed optimization algorithm (DOA) is introduced, incorporating consensus-gain function, state-dependent optimization gains, and integral information of the gradient of local objective functions. Using the It $\mathrm {\hat {o}}$ lemma and Lyapunov theory, the continuous-time DOA guarantees the $p$ th moment convergence for all agents, ensures practical finite-time consensus in probability, and drives that states of NMASs converge to the time-varying optimal solution, even in the presence of EBAMN interferences. Furthermore, a new adaptive dynamic event-triggered mechanism (ETM) integrated with the DOA is proposed. This mechanism significantly enhances communication efficiency and reduces resource consumption throughout the process of tracking the optimal solution while preventing Zeno behavior. Finally, numerical simulations in multiuncrewed aerial vehicle (UAV) target tracking validate the effectiveness of the robust continuous-time DOA against random EBAMN.

This paper investigates the resilience of autonomous vehicle (AV) systems against denial-of-service (DoS) attacks in networked environments. While existing fuzzy logic and event-triggered control strategies have been explored, they often fail to address the combined impacts of DoS-induced network congestion, data compression errors, and the lack of real-time adaptive learning to maintain stability. To address this gap, this work introduces a unified, resilient fuzzy logic control framework. First, a Performance Error Estimation (PEE) framework is developed to quantify control performance degradation under cyber threats, informing the design of the resilient controller. Second, an efficient data compression scheme is designed to mitigate DoS-induced network overload, ensuring reliable real-time data transmission under constrained bandwidth. Third, an Intelligent Event-Triggered Fuzzy Controller (IETFC) is proposed, which adaptively optimizes its triggering threshold via a mini-batch machine learning algorithm, effectively balancing communication efficiency with system robustness. Validation on the CarSim–Simulink platform demonstrates that the proposed framework successfully mitigates DoS-induced performance degradation, enhances triggering sparsity, and improves computational efficiency. By embedding learning-based adaptation into fuzzy control, this work provides a scalable and secure solution for AV systems operating under adversarial conditions.

This paper proposes Low-rank matrix factorization induced Adaptive divergent Graph learning for Fuzzy Clustering (LAGFC). This is a clustering model that unifies low-rank matrix factorization, adaptive graph learning, and fuzzy clustering to holistically exploit both global and local structural information. Unlike conventional graph-based methods that decouple graph construction and label inference into separate steps, LAGFC directly generates cluster labels by incorporating a divergence regularization term during adaptive graph learning, enabling end-to-end optimization. To enhance robustness against noisy data, the proposed method adopts the Maximum Correntropy Criterion as a distance metric, effectively suppressing outlier influence. An efficient iterative optimization algorithm, grounded in Fenchel conjugate theory and block coordinate update techniques, is developed to solve the model, and theoretical guarantees of convergence are provided. Comprehensive experiments on seven real-world image datasets demonstrate that LAGFC outperforms twelve state-of-the-art clustering methods across diverse scenarios in most cases, validating its accuracy and robustness.

This article investigates reliable leader-follower synchronization of linear multi-agent systems (MASs) under composite communication faults. Firstly, a composite double-Bernoulli link model is proposed to characterize inter-layer and intra layer communications. It captures communication mismatch by allowing independent Bernoulli link states with distinct stochastic reliabilities across the two interaction layers. Secondly, a passive predictor-reset mechanism is embedded into a modified distributed measurement to address packet losses. This mechanism efficiently leverages locally predicted surrogate values to enable uninterrupted distributed feedback computation and resets these surrogates upon data reception. Furthermore, a reliable solo adaptive protocol is developed, where each follower updates only one scalar adaptive gain together with a reliability-oriented baseline parameter. In addition, a Lyapunov-Krasovskii func tional (LKF) with an inverse gain weighting is constructed to establish the mean-square ultimate boundedness of the closed loop synchronization error. This result provides an explicit reliability guarantee under composite link faults, including low probability link activations and packet losses. Finally, numerical simulations demonstrate the effectiveness and resilience of the proposed scheme.

Concerning the stability analysis for systems with time-varying delays, a negative-determination method for a class of generalized-convex (generalized-concave) matrix-valued polynomial function is proposed, which is an extension of real-valued quadratic function negative-determination lemma. Initially, the definition of generalized-convex matrix-valued function is presented, and its first-order and second-order determination conditions are established. Subsequently, negative definite sufficient conditions for generalized-convex (generalized-concave) matrix-valued polynomials with high degrees are developed. Further, stability criteria with less conservatism for linear time-delay systems are constructed by applying the proposed method. Finally, a numerical example and a load frequency control example are presented to verify the feasibility and the advantage of the designed method on achieving less conservative stability criterion.

This article investigates the stability of delayed generalized neural networks (GNNs). First, a high-degree reciprocally convex inequality (RCI) is introduced, offering a novel method for estimating the lower bound of the reciprocally convex combination (RCC). This high-degree RCI subsumes several representative results as special cases. Second, to leverage the advantages of the high-degree RCI, a new Lyapunov–Krasovskii functional (LKF) in accordance with the application of high-degree RCI of order$r=3$is constructed. Based on the high-degree RCI and the novel LKF, a less conservative delay-dependent stability criterion is derived to ensure the asymptotic stability of delayed GNNs. Third, four widely adopted numerical examples and a study on a real-world quadruple-tank process have been conducted to demonstrate the validity and merits of the new stability criterion.

This study examines denial-of-service (DoS) attacks on networked control systems (NCSs) caused by TCP/IP protocol vulnerabilities. It conducts a comprehensive analysis of hacker tactics to uncover vulnerability exploitation techniques. The research reformulates the performance error estimation (PEE) problem, framing it as a search for ellipsoid constraints within a P-dependent set. It introduces the Higher-Order Weight Method (HOWM) to optimize sampling intervals, leveraging the unique properties of zero-order-hold sampling. The study enhances Lyapunov-Krasovskii functions (LKFs) using HOWM by partitioning integral terms and applying quadratic scaling to optimize control algorithms, thereby reducing conservatism and tightening upper-bound criteria. Additionally, it proposes an innovative Integral Event-Triggered Control (IETC) strategy for estimating system performance errors. The performance of the proposed control algorithm is rigorously evaluated through simulations on a complex 2-degree of freedom (DoF) helicopter system (HS). Note to Practitioners—This study delves into DoS attacks targeting NCSs due to TCP/IP protocol vulnerabilities. It conducts an exhaustive analysis of hacker tactics to uncover techniques used to exploit these vulnerabilities. The research reframes the PEE problem, casting it as a search for ellipsoid constraints within a P-dependent ellipsoidal set. It introduces the HOWM to optimize sampling intervals, leveraging the unique characteristics of zero-order-hold sampling. Additionally, the study enhances LKFs using HOWM by partitioning integral terms and applying quadratic scaling to optimize the control algorithm, thereby reducing conservatism and tightening upper-bound criteria. Moreover, it introduces an innovative IETC strategy for estimating system performance errors. The performance of the proposed control algorithm is rigorously evaluated through simulations on a complex 2-DoF HS.

This paper studies the stability problem of sampled-data-based load frequency control (LFC) doubly fed induction generator (DFIG)-integrated wind power system (WPS). Firstly, a unified fuzzy DFIG-integrated WPS model is constructed by analyzing the nonlinear aspects of governor and turbine dynamics. Secondly, a sampled-data-based fuzzy proportional-integral control strategy (FPICS) is designed for stabilizing the power system. Thirdly, some new stability criteria are established by Lyapunov theory. Additionally, an improved trapezoidal algorithm is proposed to process the integral term in the controller, which can effectively reduce the consumption of computing resources. Finally, the effectiveness of proposed algorithm and the FPICS are verified through simulations. Note to Practitioners—Due to nonlinearities in the power system arising from the physical limitations of non-reheat governors, this paper aims to design an appropriate control strategy to address the nonlinear issues arising from governor valve position limiting, which may adversely affect the stability of the power system. Specifically, we propose an FPICS to tackle the nonlinearities caused by governor valves. Besides, an improved trapezoidal algorithm, which can dynamically determine and utilize the minimal number of subintervals according to the controller output variation, is designed to fit the numerical values of the controller’s integral term. This ensures control signal accuracy while maximizing computational resource savings. Moreover, to enhance the system’s tolerance to significant delays and disturbances, this paper considers the time-varying delay in the controller and establishes new stability criteria. Finally, through theoretical analysis and simulation cases, the effectiveness and reliability of the proposed fuzzy control strategy and improved trapezoidal algorithm are validated.

This paper studies the fuzzy-based synchronization control problem of coupled neural networks under cyber attacks, where the considered attacks can block the communication links between fuzzy sub-neural networks. Firstly, an improved fuzzy network model is developed, which takes into account the inherent vulnerabilities of network. We design a fuzzy logic-based event-triggered delayed impulsive controller, where impulsive signals are generated by a dependent-Lyapunov intelligent impulsive selection algorithm. Particularly, it can mitigate attack effects, ensure the desired performance of fuzzy networks, and effectively exclude the Zeno behavior. Then, based on the proposed algorithm, some delay-dependent synchronization criteria are established for fuzzy networks on different scales delays, respectively. Finally, a practical example about resistance-capacitance circuit network is provided in different scenarios to show the validity of the theoretical results.Note to Practitioners—This paper was motivated by existing results on impulsive synchronization control of neural networks. Most existing results ignore the effects of external cyber attacks and delay during signal transmission, which is quite difficult to simulate the practical network model. This paper constructs a more general model to consider the inherent vulnerabilities of networks. Then, an intelligent impulsive selection algorithm is designed to resist the risks of attacks and obtain the expected performance. The obtained results are applied to resistance-capacitance circuit systems to verify the effectiveness, and it is expected that the proposed approach can be extended to more mechanical systems.

This paper explores the observer-based event-triggered security control problem for linear systems. Initially, a distributed dynamic-memory event-triggered scheme (DMETS) is proposed based on observed system states. This scheme is designed using information from past transmitted data packages, with the flexibility to dynamically adjust the quantity of transmitted data packages. Subsequently, a comprehensive mathematical model of hybrid cyber-attacks is established, and an observer-based distributed feedback control approach is proposed by utilizing the memory states. Furthermore, the asymptotic stability criterion of system is established by using Lyapunov direct method, and the design approaches of controller and observer are provided. Finally, contrast illustrative examples are presented to validate the advantage of DMETS and the effectiveness of the distributed control method. Note to Practitioners—This paper is prompted by the challenges of excessive consumption of network resources and the need for enhanced security control in linear systems. The prevailing approach to addressing this issue involves designing appropriate data transmission schemes, specifically ETS. However, a common limitation among these existing ETSs is the inability to efficiently utilize past transmitted data or dynamically adjust the quantity of data utilized in response to changes in system states. To overcome this limitation, we propose a DMETS, which aims to integrate memory characteristics and facilitate dynamic adjustments in memory data quantities.

This paper investigates the frequency regulation problems of integrated renewable energy sources (RESs) power system under the non-ideal environment in open communication networks. Some problems are raised up: (i) The integration of high penetration levels RESs into the power system reduces the inertia characteristics and degrade frequency regulation performance. (ii) The non-ideal network environment disrupt data transmission and deteriorate control accuracy. To address the above problems, a load frequency control (LFC) with virtual synchronous generator method (VSG-LFC) is presented, which greatly reduces the power system’s sensitivity to the level of sustainable energy penetration. Then, a hybrid trigger mechanism (HTM) consisting of a time trigger mechanism and an improved adaptive event trigger mechanism is adopted to handle the unforeseen environment influence with less occupied network bandwidth. Meanwhile, theoretical analysis on the frequency stabilization of power system is conducted by using looped-functional method and the related knowledge. In simulation evaluation, the proposed HTM along with VSG-LFC scheme outperforms the convention LFC subject to the introducing of RESs. Specifically, the VSG-LFC scheme achieves reductions of 98.9892% in IAE, 98.9234% in ITAE, and 87.0486% in ISE compared to conventional LFC.

This paper investigates the dual-channel event-triggered quantitative load frequency control for discrete-time micro-grid system via a delay-square-dependent Lyapunov-Krasovskii functional approach. Firstly, a novel dual-channel dynamic event-triggered quantified control strategy has been proposed to enhance time-delay tolerance and the utilization of communication resources. Secondly, a discrete-time Lyapunov-Krasovskii functional incorporates the square of the delay is introduced to fully deploy the desired delay information about the system. Thirdly, improved stability criteria with strict dissipative performance indexes are established by employing the proposed delay-square-dependent Lyapunov-Krasovskii functional. Finally, two numerical examples are presented to illustrate the effectiveness and superiority of the proposed control strategy.Note to Practitioners—Micro-grid typically consist of distributed power resources, loads and energy storage devices, those are deployed spatially and remotely over some wireless and digital network mediums. The constrained resource issue, posed by finite bandwidth and processing capabilities of battery-powered system components. This paper aims at designing a reasonable control strategy to deal with issues such as band-limited channels and communication delays that undermine system stability in the process of network transmission. Specifically, we propose a dual-channel asynchronous dynamic event-triggered control strategy to save the limited channel bandwidth. To mitigate the impact of time delays, we comprehensively consider the state-dependent delays and control-related delays, further establishing a novel Lyapunov functional incorporating delay squared terms. The proposed control approach is validated to be capable of better reduce the consumption of bandwidth and achieve the desired stability through theoretical investigation and simulation cases.

This paper concerns the observer-based dual-memory control for Lurie-type cyber-physical systems (CPSs) subject to denial of service (DoS) attacks. First, an augmented switching Lurie-type CPSs model is established to analyze the impact of DoS attacks and synergy time delays on controller performance. Second, the memory-dynamic event-triggered scheme (MDETS) efficiently utilizes bandwidth resources by adapting the number of historical data packets transmitted based on system state fluctuations, meanwhile, the controller design incorporates constant time-delay compensation to improve system stability. Third, the exponential stability conditions for the closed-loop Lurie-type CPSs are derived via Lyapunov theory. Additionally, the co-design of the observer gain, controller gain, and event-triggering matrices is formulated using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed control scheme is validated through application to a DC motor system.

This article focuses on the dissipative stability issue with load frequency control (LFC) in multiple time-varying delay (MTD) power systems. Firstly, the model is constructed considering the effects of MTD. Next, Lyapunov-Krasovskii functionals (LKF) are constructed which include delay-product-type terms (DPT) with MTD. Then, the low conservative stability criteria with respect to linear matrix inequalities (LMIs) are derived through the application of auxiliary function-based integral inequalities (AFBII) and improved reciprocal convex inequalities (IRCI) to handle the DPT. Next, an effective dissipative control strategy is developed based on the above criteria. Finally, simulation experiments are carried out, and the effectiveness of the proposed design method is highlighted by comparing with the previous results. Note to Practitioners—This paper focuses on the dissipative stability of LFC in power systems with MTD. It provides a new perspective for the control and optimization of power systems and other dynamic systems involving MTD. Specifically, when constructing LKF with DPT, this paper designs for different time-varying delay conditions, which can better deal with the uncertainty caused by time delay changes and help to improve the accuracy and reliability of system stability analysis. Then, the new stability criterion proposed in this paper provides more relaxed conditions, which reduces the conservatism of system stability analysis and makes the stability criterion more practical application value. Based on the new stability criterion, a dissipative controller design method is presented in this paper. This method can provide better control effect when dealing with complex multi-delay systems and help to improve the overall operating efficiency and stability of the power system. Finally, the effectiveness of the scheme is verified by simulating the multi-region power system.

This paper considers intelligent event-triggered${H}_{\infty }$Load Frequency Control (LFC) for a new kind of multi-area power system. Firstly, delay factors are introduced into the prime mover, engine, and energy storage unit, i.e, multiple-resource delays, and the impact of these delays on the dynamic behavior of system is discussed for the first time, making the model more realistic and accurate. Secondly, a new lemma is proposed, which introduces free variables, extending to the scenarios with time delays. Combining this lemma with the event-triggered mechanism, the control scheme is designed, which is featured by two characteristics. On the one hand, in the Lyapunov functional, looped terms on neighbor triggering instants are considered, reduce the conservatism of the obtained criteria by capturing the information of triggered instants. Meanwhile, a mandatory triggering mechanism is introduced to break the possible dead loop caused by long-term no-triggering. On the other hand, a genetic algorithm (GA) is employed to optimize the parameters of the trigger threshold, encouraging efficient control within the context of LFC and delays. The effectiveness of the proposed approach is confirmed by a typical numerical example with four case studies.

This article addresses the challenge of achieving asymptotic stability in nonlinear networked control systems (NNCSs) amid denial-of-service (DoS) attacks, particularly under constrained communication resources. We begin by establishing a practical DoS attack model using the NSL-KDD dataset, which provides a realistic depiction of DoS attack dynamics based on real-world data. We then introduce the iterative shrinkage-thresholding algorithm (ISTA) to supervise the adaptive event-triggered controller (AETC), ensuring that system parameters are adjusted effectively while conserving communication resources. We develop an enhanced data compression mechanism to further mitigate the impact of DoS attacks on communication servers. Additionally, we construct an asymmetric Lyapunov-Krasovskii function (LKF) to rigorously verify the asymptotic stability of NNCSs. Finally, we empirically validate the effectiveness of our proposed AETC using an autonomous vehicle (AV) model.

This article presents a novel event-triggered sliding-mode control (ET-SMC) strategy for impulsive nonlinear systems (INS) in the presence of matched disturbances. Most of the existing sliding mode control (SMC) strategies work well when the system continually converges toward a predefined sliding surface, but have been proven to be inapplicable for discontinuous systems subjected to impulsive disturbances. Consequently, it becomes crucial and imperative to develop SMC strategies tailored for discontinuous dynamics affected by impulsive phenomena. Leveraging the event-triggering mechanism with a time-varying threshold and incorporating piecewise Lyapunov function techniques, we propose a novel ET-SMC strategy. It is proved that the proposed control strategy can effectively avoid Zeno behavior and ensure the finite-time stability (FTS) of the considered system via finite-time control theory and innovative impulsive estimation schemes. Furthermore, our results quantitatively measure changes in convergence rate under varying impulsive conditions, which provides valuable insights for performance analysis and the design of SMC strategies in such scenarios. As a specific application, we apply the proposed ET-SMC strategy to coupled reaction-diffusion neural networks (RDNNs) in the presence of both cyber-attacks and matched disturbances. Finally, we present several simulation examples to illustrate the effectiveness and practical applicability of the analytical methods presented in this article.

The integration of renewable energy sources increases the vulnerability of power systems to cyber-physical threats, particularly denial-of-service (DoS) attacks that exploit transient instabilities caused by renewable generation fluctuations. These attacks disrupt communication channels, leading to cascading frequency deviations and increased risk of system instability. To address this challenge, we propose a multi-layer defense framework integrating attack modeling, adaptive control, and stability guarantees. First, a frequency-driven DoS attack model is constructed by analyzing frequency trends to identify vulnerable regulation periods and support proactive threat prediction. Second, a hybrid information-based reinforcement learning (HIRL) strategy is developed to adaptively optimize transmission intervals under DoS attacks, balancing regulation performance and communication efficiency. Third, a Takagi–Sugeno (T-S) fuzzy load frequency control with virtual synchronous generator (VSG-LFC) scheme is designed to enhance frequency stability through inertia emulation, mitigating both renewable intermittency and attack-induced oscillations. Lyapunov-based stability analysis is conducted to establish theoretical guarantees for the coupled cyber-physical system. Finally, simulation results on the New England 9-bus system demonstrate the effectiveness and practical applicability of the proposed framework, showing superior frequency stability compared to conventional methods.

This paper addresses the critical challenge of achieving asymptotic stability in networked control systems (NCSs) under denial-of-service (DoS) attacks, focusing on maintaining security and stability within bandwidth-constrained environments. First, we construct a practical attack model using the NSL-KDD dataset to provide a realistic representation of DoS attack dynamics, capturing key attributes such as attack duration and frequency. Then, an iterative shrinkage-thresholding algorithm (ISTA) is introduced to supervise the adaptive sampled-data controller (ADSC), dynamically optimizing the sampling period to enhance control performance while minimizing communication overhead. To further mitigate the impact of DoS attacks, we propose a novel data compression mechanism that adapts to varying network conditions, ensuring efficient bandwidth utilization and preserving critical control data fidelity. In addition, the stability of the NCS is rigorously verified through Lyapunov-Krasovskii functions (LKFs), demonstrating robust system behavior even under adverse network conditions. Finally, the effectiveness and practicality of the proposed approach are validated through experimental studies on a 2-degree-of-freedom (2-DoF) helicopter system, confirming its capability to ensure stability, optimize communication efficiency, and mitigate the effects of DoS attacks in real-world scenarios.

This paper investigates the stability of a delayed load frequency control (LFC) power system. Firstly, a dynamic LFC model is reconstructed to facilitate the development of a suitable Lyapunov-Krasovskii functional (LKF), which enables the derivation of stability conditions with reduced numerical complexity. Electric vehicles (EVs) are also incorporated into the LFC model to help mitigate frequency fluctuations. Secondly, a parameter-adjustable cubic function negative-determination condition is proposed, generalizing the existing condition in the literature as a special case and reducing its conservatism. Then, by combining the proposed negative-determination condition with the suitable LKF, new stability conditions are derived, enabling a more accurate determination of the allowable delay upper bound (ADUB). Furthermore, to improve computational efficiency in determining the ADUB, the suitable LKF not only provides stability conditions with lower complexity, reducing time per iteration, but also incorporates a binary search algorithm, significantly minimizing the total number of iterations required. Finally, case studies are presented to demonstrate the effectiveness of the proposed methods.

This article focuses on investigating the stability issue for recurrent neural networks (RNNs) with interval time-varying delays (TVDs) based on a flexible delay-dividing method with parameters, which are related to the delay derivative. First, an interval of delay is separated into parametric subintervals via the linear combination technique. Then, an establishment of Lyapunov-Krasovskii functional (LKF) is connected to the parameters, and a novel linear technology is suggested to dispose of integral terms in the derivatives of the constructed function. Finally, the validity and advantage of the inferred criteria are interpreted by the comparison of representative simulation examples.

As a pivotal subfield within the domain of time series forecasting, runoff forecasting plays a crucial role in water resource management and scheduling. Recent advancements in the application of artificial neural networks (ANNs) and attention mechanisms have markedly enhanced the accuracy of runoff forecasting models. This article introduces an innovative hybrid model, ResTCN-DAM, which synergizes the strengths of deep residual network (ResNet), temporal convolutional networks (TCNs), and dual attention mechanisms (DAMs). The proposed ResTCN-DAM is designed to leverage the unique attributes of these three modules: TCN has outstanding capability to process time series data in parallel. By combining with modified ResNet, multiple TCN layers can be densely stacked to capture more hidden information in the temporal dimension. DAM module adeptly captures the interdependencies within both temporal and feature dimensions, adeptly accentuating relevant time steps/features while diminishing less significant ones with minimal computational cost. Furthermore, the snapshot ensemble method is able to obtain the effect of training multiple models through one single training process, which ensures the accuracy and robustness of the forecasts. The deep integration and collaborative cooperation of these modules comprehensively enhance the model's forecasting capability from various perspectives. Ablation studies conducted validate the efficacy of each module, and through multiple sets of comparative experiments, it is shown that the proposed ResTCN-DAM has exceptional and consistent performance across varying lead times. We also employ visualization techniques to display heatmaps of the model's weights, thereby enhancing the interpretability of the model. When compared with the prevailing neural network-based runoff forecasting models, ResTCN-DAM exhibits state-of-the-art accuracy, temporal robustness, and interpretability, positioning it at the forefront of contemporary research.

In islanded AC microgrids, achieving optimal power distribution across distributed generators (DGs) and restoring frequency post-primary control under limited communication resources is paramount for ensuring stability and flexibility. In this paper, we develop a distributed adaptive secondary control strategy tailored for islanded AC microgrids. This strategy facilitates effective active power sharing and frequency regulation using solely local network information. A unique adaptive edge-based event-triggered transmission mechanism is also proposed, obviating Zeno behavior, to reduce communication overhead. It requires only the degree information of a single node in the DG network topology. Contrasting most existing research that necessitates global network topology details, like the Laplacian matrix, this work establishes a fully distributed control performance criterion that operates devoid of global data. The effectiveness of the proposed control approach is validated using a modified IEEE 34-bus test system.

This article investigates the security consensus problem for fractional-order multiagent systems (FOMASs) with cyber attacks via the hybrid event-triggered and impulsive control strategy. First, based on the destructive characteristics of network attacks on communication channels, a method for separating partial network structures is proposed. Differing from other studies of multiagent systems, it considers here that the communication topological network consists of strong channel and weak channel networks, and its connectivity is not required. Then, to overcome bandwidth constraints, a new hybrid event-triggered and impulsive security consensus control approach is first designed, which only needs to detect the sampled data at instants of cyber attacks and impulse. Furthermore, applying the fractional Lyapunov stability analysis methods, several sufficient conditions are proposed to achieve the exponential consensus for FOMASs with cyber attacks. Meanwhile, some parameters in security consensus control protocol are acquired. Finally, from the perspective of numerical simulation visualization, the rationality and correctness of the proposed consensus security control method are verified.

In this article, an optimal tracking control algorithm is derived for second-order discrete-time multiagent systems (MASs) with unknown system dynamics and input delay. First, the optimal tracking problem of MASs with input delay is constructed by the tracking error and a local performance index function. By designing a new variable, the original model is converted into a model without delay while guaranteeing the equivalence of performance index and control law of each agent. Subsequently, the transformed model and reinforcement learning (RL) theory are integrated to obtain a novel data-driven distributed learning framework. This framework enables online learning of the optimal control law and ensures tracking consensus of all followers’ position and velocity states. Compared to the traditional actor–critic framework, an additional neural network (NN) is utilized to approximate the forward reward information (FRI) to improve the information learning capability of the MASs. Furthermore, the convergence analysis of system states and three NNs structures are conducted by Lyapunov theory. Finally, the proposed framework is verified to have better convergence and require fewer iteration steps than classical actor–critic framework by numerical simulation comparison experiments.

This study addresses the frequency control problem in multiarea power systems through the design of a decentralized periodic event-triggered scheme (DPETS). Initially, a unified discrete-time multiarea model is developed for power systems by adopting the general discretization method. Subsequently, a discrete-type DPETS is designed, relying solely on the locally available area control error to determine the time instants for transmitting the control signal to the equivalent generating unit. Furthermore, the ultimate boundedness of power systems under time-varying load demand disturbance is demonstrated by constructing novel discrete-time Lyapunov-Krasovskii functionals, leading to a rigorous bound of ultimate states. Finally, a case study involving three-area interconnected power systems is presented to validate the feasibility of the designed control strategy.

This paper introduces an algorithm to construct a bidirectional causal graph using an augmented graph. The algorithm decomposes the augmented graph, significantly reducing the size of the variable set required for conditional independence testing. Simultaneously, it preserves the fundamental structure of the augmented graph after decomposition, saving time and cost in constructing a global skeleton graph. Through experiments on discrete and continuous datasets, the algorithm demonstrates a clear advantage in runtime compared to traditional methods. In large-scale sparse networks, the training time is only about one-tenth of traditional methods. Additionally, the algorithm shows improvement in terms of construction error. Since the input to the algorithm is an augmented graph, this paper also discusses the impact on construction error when using both real and generated augmented graphs as input. Furthermore, the concept of markov blanket is extended to multivariate regression chain graphs, providing a method for rapidly constructing augmented graphs given certain prior knowledge.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

This article presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems caused by denial-of-service (DoS) attacks and quality-of-service (QoS) queuing mechanisms. We develop a novel data compression mechanism to alleviate network congestion and use a mini-batch descent gradient algorithm to optimize trigger thresholds, thereby reducing bandwidth usage. In addition, we introduce asymmetric Lyapunov–Krasovskii functions to decrease the number of decision variables, which improves the reliability and robustness of the control algorithm. Finally, we propose an intelligent event-triggered controller supervised by mini-batch machine learning and validate it on the joint CarSim–Simulink platform. Experimental results demonstrate that our approach reduces the sensitivity of autonomous vehicle systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.

This paper focuses on distributed sliding-mode consensus tracking control of fuzzy multi-agent systems (MASs) under time-varying transmission delays and hybrid cyber-attacks, including deception and denial-of-service (DoS) attacks. Impulsive and switching signals are adopted to describe the dynamic process of the fuzzy MAS subject to hybrid cyber-attacks. In order to address the hybrid cyber-attacks, a novel integral-type sliding surface function grounded on lumped consensus tracking errors is presented, and a resilient control scheme based on this surface is designed accordingly. By adopting the analysis method of hybrid systems and finite-time control theory, sufficient conditions are deduced to guarantee that the tracking error dynamics can achieve the intended sliding surface in a finite time and remain on the surface thereafter. Meanwhile, the quantitative relationships between the influence of hybrid cyber attacks on the convergence rate of the control strategy are clearly revealed. Moreover, the stabilities of the corresponding sliding mode dynamics are investigated. Finally, the example of cooperative fuzzy robot manipulators is used to illustrate the effectiveness and validity of the proposed theoretical results.

This article is centered on the synchronization issue of fuzzy reaction–diffusion dynamic networks (RDDNs) with multiple cyberattacks. A novel attack-resilient dynamic event-triggered policy and the relevant fuzzy attack-resilient controller are designed via considering the deception and irregular (aperiodic) denial-of-service (DoS) attacks simultaneously. Based on a novel piecewise Lyapunov–Krasovskii functional with the realistic sampling information and some inequality techniques, some criteria are derived to ensure that the synchronization of fuzzy RDDNs can be realized in the presence of the deception and irregular DoS attacks. Lastly, simulation examples containing the colour image encryption/decryption are supplied to exhibit the advantages and practicality of the developmental theories.

This article investigates the stability and stabilization problem for delay-dependent Takagi–Sugeno (T–S) fuzzy load-frequency control (LFC) power system with energy storage system (ESS). First, a unified T–S fuzzy LFC model is constructed for power system with ESS by further analyzing the nonlinear characteristics existing in turbine and governor dynamics. Second, a fuzzy proportional-integral control strategy is proposed to stabilize the T–S fuzzy power system. Then, based on existing approaches to handle quadratic function with respect to the time-varying delay, an improved quadratic function negative-determination lemma is proposed to achieve larger upper bound of time delay. Furthermore, delay-dependent stability criteria with less conservatism are established by means of Lyapunov stability theorem. Finally, some contrast illustrative examples are given to verify the advantage of the proposed methods.

This study investigates the stability problem in nonlinear networked control systems (NCSs). First, innovative compression rules are introduced to mitigate network congestion and bandwidth utilization issues stemming from quality of service (QoS) queuing mechanisms and denial of service (DoS) attacks. We develop an intelligent trigger controller supervised by a mini-batch machine learning (MBML) algorithm to optimize network bandwidth utilization. Furthermore, we formulate more generalized Lyapunov-Krasovskii functions (LKFs) to simplify mathematical derivations, and we employ appropriate integral inequalities to minimize constraints. Finally, experimental evaluations are conducted on an autonomous vehicle (AV) using the joint CarSim-Simulink platform to verify the effectiveness of the proposed intelligent trigger controller.

This short paper addresses the challenge of network congestion in T-S fuzzy networked control systems (NCSs) caused by denial of service (DoS) attacks and quality of service (QoS) queuing mechanisms. Firstly, a novel signal compression mechanism is introduced to mitigate network congestion. The trigger threshold is optimized using a mini-batch descent gradient algorithm, effectively reducing bandwidth utilization. Furthermore, tailored Lyapunov-Krasovskii functions (LKFs) are established for the system, and we propose an intelligent event-triggered controller (IETC) under machine learning supervision. Finally, the effectiveness of the proposed approach is demonstrated through rigorous verification on the joint CarSim-Simulink platform.

This paper studies the secure sampled-data consensus of multi-agent systems (MASs) under asynchronous independent deception attacks and multiplicative control gain disturbances by developing the looped Lyapunov-Krasovskii functional (LKF) approach. Because of the openness and shareability of communication network, as well as the time delay in the process of data transmission, two independent deception attacks with asynchronous phenomenon are considered in the sensor-to-controller (SC) and controller-to-actuator (CA) channel of MAS communication network. Furthermore, a coupled memory-based sampled-data control scheme is proposed and fractional-delay states on sampling intervals are introduced to construct an improved delay-dependent looped LKF. Therefore, a relaxed condition with lower conservatism is established to asymptotically ensure the secure sampled-data consensus of MASs in mean square under our designed control protocol. Finally, a simulation test applied to unmanned surface vehicles is conducted to validated the performance of designed control strategy.

This article investigates the resilient proportional-integral observer (PIO) problem for Markov switching memristive neural networks (MSMNNs) with randomly occurring sensor saturation within a finite-time interval. The Markov switching of memristive neural networks is regulated by a higher level deterministic switching signal, whose transition probabilities are piecewise time-varying and can be depicted by the average dwell-time strategy. Meanwhile, a Bernoulli stochastic process associated with an uncertain packet arriving rate is adopted to describe the randomly occurring sensor saturation. The aim is to design a resilient PIO such that the augmented dynamic has the property of stochastic finite-time boundedness while meeting the desired performance index. By applying the Lyapunov method and the average dwell-time scheme, sufficient criteria are established for MSMNNs, and a unified design method is presented for the existence of the PIO. Lastly, the attained theoretical results are validated via a numerical simulation.

This article is concerned with the fixed-time stability (FTS) problem of nonlinear impulsive systems (NISs). By means of the impulsive control mechanism and Lyapunov functions theory, several sufficient conditions are established to ensure the FTS of general NISs. Meanwhile, some novel impulse-dependent settling-time estimation schemes are developed, which fully considers the influence of stabilizing impulses and destabilizing impulses on the convergence rate of the system states. The proposed schemes establish a quantitative relationship between the upper bound of the settling time and impulse effects. It shows that stabilizing impulses can accelerate the convergence rate of the system states and leads to the upper bound of the settling time being smaller. Conversely, destabilizing impulses can reduce it and make the upper bound of the settling time larger. Then, the theoretical results are applied to delayed inertial neural networks (DINNs), where two kinds of controllers are designed to realize fixed-time synchronization of the considered systems in the impulse sense. Finally, some numerical examples are provided to illustrate the validity of the proposed theoretical results.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU). But, the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption; to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled “block partition” schemes are proposed. However, these methods are coarse-grained or static and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model [long short-term memory (LSTM), combined with the dynamic-fill scheme] generates remarkable mapping performance on the small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.

This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches. Furthermore, the mode-mixed effects caused by the intralayer coupling delays and switched intralayer topologies are incorporated into the novel model and analysis method to ensure that the subsystems operating within the current switching interval can effectively use the topology information of the previous switching intervals. Then, a novel analysis framework including super-Laplacian matrix, augmented matrix, and mode-mixed methods is developed to derive the synchronization results. Finally, the main results are verified via the numerical simulation with secure communication.

This article is devoted to dealing with exponential synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) under the framework of aperiodic sampling and state quantization. First, by taking the effect of aperiodic sampling and state quantization into consideration, a novel quantized sampled-data (QSD) controller with time-varying control gain is designed to tackle the exponential synchronization of INNs. Second, considering the available information of the lower and upper bounds of each HTVD, a refined Lyapunov-Krasovskii functional (LKF) is proposed. Meanwhile, an improved looped-functional method is utilized to fully capture the characteristic of practical sampling patterns and further relax the positive definiteness requirement for LKF. Consequently, less conservative exponential synchronization conditions with extra flexibility are derived. Finally, a numerical example is employed to demonstrate the effectiveness and advantages of the proposed synchronization method.

This article addresses the issue of mismatched communication delay (CD) in dual-channel nonlinear networked control systems (NCSs) resulting from the quality of service (QoS) mechanism’s queue management. The focus is on the importance of ensuring communication security in NCSs, particularly in the presence of asynchronous cyber attacks (ACAs). First, improved Lyapunov–Krasovskii functions (LKFs) are constructed, taking into account sampling signals, CDs, and nonlinearities in the system. Additionally, a novel looped-functional approach is introduced to reduce the initial constraint of the criterion. Then, the control algorithm’s performance is optimized by achieving a tighter upper bound on the integral term and applying quadratic scaling. To ensure stability and security under ACAs, a fuzzy memory sample-data controller (MSAC) is proposed. This controller leverages a machine learning algorithm to address the optimization problem of data sampling period selection, thereby minimizing resource usage and operating efficiently within the system’s limited bandwidth. Finally, numerical simulations are conducted using the dynamic equations of the inverted pendulum system (IPS) to validate the practicality of the proposed theoretical approach.

In this article, a dynamic event-triggered adaptive neural network decentralized nonrecursive output-feedback control scheme for nonlinear interconnected systems under hybrid cyber attacks is first proposed, where the hybrid cyber attacks, containing deception attacks and denial-of-service (DoS) attacks, obey Bernoulli distribution. Taking into account the restrictions imposed by network transmission, a dynamic event-triggered mechanism is introduced to fulfill the analysis and design task. A decentralized linear observer is established to estimate the unknown states. Meanwhile, a decentralized adaptive output-feedback controller is developed in a nonrecursive method with the help of neural network technology. The proposed control scheme can ensure that all signals in the closed-loop system are bounded. Furthermore, Zeno phenomenon can be effectively avoided. Finally, simulation results validate the feasibility of the proposed control scheme.

This study is to focus on the problem of finite-time control design for a class of networked control systems (NCSs) with mixed delays. First, a novel hybrid dropout compensation control method is proposed to reasonably use available system feedback information, which can actively compensate for data packet dropouts in two communication channels. Second, an improved augmented switched system model is constructed based on the mixed delays and the hybrid parameter. Then, the gain matrices that vary with the data packet dropout are formulated to ensure stochastic finite-time stability (SFTS) and improve the transient control performance of the resulting closed-loop system. The slack variables (SVs)-based algorithm is presented to reduce the computational time, and the average dwell-time switching law is determined. Finally, the obtained theoretical results are verified by a numerical example.

In this article, a novel adaptive self-triggered controller is constructed to handle the fault-tolerant control problem for nonlinear system with input dead zone through a funnel function method: 1) the actuator failure and the dead-zone nonlinearity are fully analyzed for the modeling purpose, we transform the system into a new model; 2) the self-triggered scheme is set up to update the control signals, which effectively economizes communication resources; 3) due to the existence of unknown control gains and unknown nonlinear functions, the adaptive neural network funnel controllers are designed, such that the tracking error is confined within the funnel boundary and all the signals of the closed-loop system are semi-globally uniformly ultimately bounded; and 4) finally, the applicability of the acquired control algorithm is demonstrated via a rigid robot manipulator system.

This article investigates the finite time bumpless transfer fuzzy control problem for Markovian jump fuzzy systems (MJFSs) via an observer-based bumpless transfer fuzzy control approach. Neither the measurability of system states nor the availability of all transition rates for each subsystem is required. A bumpless transfer index with a continuous reference input is derived to characterize the transient jumping behavior of MJFSs caused by an observer-based fuzzy jumping controller. First, a Markovian jump fuzzy model is employed to describe the more general practical situations. Second, by introducing a fuzzy state observer, an observer-based bumpless transfer fuzzy jumping controller is established, and a sufficient condition is developed such that the finite time fuzzy control issue for MJFSs is solvable while ensuring the bumpless transfer index. Finally, two simulation examples are provided to verify the utilizability of the acquired method.

This article analyzes the frequency regulation problems of load frequency control (LFC) system under some unforseen network environments, such as unexpected load, mixed attacks, and time delay in cyber–physical system (CPS). A dual-link strategy using alternating current and direct current tie-line transmission technologies is employed to improve the efficiency of power exchange and retain the power system frequency stability. Then, an improved integral-based adaptive event-triggered control (IA-ETC) with flexible threshold parameters is designed using the historical average system state to tackle the sudden fluctuations with improved communication efficiency. Furthermore, the Lyapunov–Krasovskii functional (LKF) with delay terms is proposed to investigate the exponential stability with$H_{\infty}$performance on the dual-link LFC system. Finally, simulation results show that this development presents an improved frequency regulation performance.

Runoff forecasting is the key to the rational use and protection of water resources by mankind. The large-scale application of machine learning and neural networks in hydrological models has made accurate and reliable short-term runoff forecasting possible. In this article, a novel short-term runoff forecasting framework called ResGRU Plus is proposed with gated recurrent unit (GRU) as the backbone. GRU has the characteristics of long short-term memory (LSTM) that can selectively memorize and forget information while merging gating units to reduce the amount of parameters. Residual network (ResNet) is also deeply integrated with GRU, and its unique shortcut connection effectively solves the degradation problem of traditional neural networks, making it possible to train deep neural networks based on the recurrent architecture. Moreover, a lightweight attention mechanism module: squeeze-and-excitation network (SENet) is embedded in the framework. SENet explicitly models the interdependence between feature dimensions through one global average pooling layer (GAP) and two fully connected (FC) layers, and rescales the original features through the learned weights to adaptively amplify or suppress features. Snapshot ensemble method is also used to train ResGRU Plus, which can integrate multiple homogeneous weak learners through one training process to improve the performance of the model at a small cost. In this article, the hourly runoff of the Columbia River is used as the data set. The Nash–Sutcliffe coefficient (NSE) of Efficiency and coefficient of determination$(R^{2})$, which are two common evaluation metrics for hydrological models, are used to measure the performance of the models. Multiple sets of ablation experiments show that the proposed ResGRU Plus, which combines ResNet and the attention mechanism, is able to improve depth by a factor of over 4 and accuracy by nearly 18% compared to the vanilla GRU, which further fully validates the effectiveness of combining the residual structure and attention mechanism with the recurrent architecture-based neural network and the feasibility of applying it to runoff forecasting. In addition, several sets of comparative experiments have also demonstrated the state-of-the-art performance of ResGRU Plus with significant improvement in accuracy compared to mainstream time-series forecasting models.

In this work, the non-fragile output feedback control problem is discussed for persistent dwell-time switched nonlinear systems . In order to make full use of the bandwidth in communication networks, the weighted try-once-discard protocol is adopted to connect only one sensor with communication networks at a instant via the maximum-error-first policy. It is worth noticing that via using the weighted try-once-discard protocol, the information received by the controller becomes less such that the gains of the controller may be more susceptible to the internal and external environment. Therefore, the non-fragile output-feedback fuzzy control scheme is employed for maintaining the performance of controllers under such a protocol. Then, based on suitable augmented multiple Lyapunov-like functions, sufficient conditions are derived to ensure the stochastically globally uniform exponential stability and a fixed H ∞ performance for the resulted closed-loop system. Furthermore, through solving those conditions, the design approach of the desired controller can be developed. The feasibility of the established approach is illustrated via a example and a truck-trailer model.

This paper studies the secure aperiodic sampling control issue for Micro-Grids(MGs) under abnormal deception cyber attacks (ADCAs). Firstly, a novel relaxed condition that depends on the time delay (TD) is constructed, further reducing the existing constraint condition. Secondly, a new bilateral delay-dependent looped-functional (BDDLF)$V_{c}(x_{t})$is developed. Thirdly, based on the characteristics of the aperiodic sampling control, another improved BDDLF$V_{d}(x_{t})$is developed for acquiring more state information. Furthermore, an optimized control algorithm is designed using proper integral inequalities and the convex combination method. Then, a new secure aperiodic sampled data (SASD) controller under ADCAs is achieved to guarantee real power-sharing between distributed generators (DGs) and energy storage systems (ESSs) in MG. Finally, simulation experiments are carried out on MG to verify the effectiveness and feasibility of the designed control algorithm.

This article dedicates to automatically explore efficient portrait parsing models that are easily deployed in edge computing or terminal devices. In the interest of the tradeoff between the resource cost and performance, we design the multiobjective reinforcement learning (RL)-based neural architecture search (NAS) scheme, which comprehensively balances the accuracy, parameters, FLOPs, and inference latency. Finally, under varying hyperparameter configurations, the search procedure emits a bunch of excellent objective-oriented architectures. The combination of two-stage training with precomputing and memory-resident feature maps effectively reduces the time consumption of the RL-based NAS method, so that we complete approximately 1000 search iterations in two GPU days. To accelerate the convergence of the lightweight candidate architecture, we incorporate knowledge distillation into the training of the search process. This also provides a reasonable evaluation signal to the RL controller that enables it to converge well. In the end, we conduct full training with outstanding Pareto-optimal architectures, so that a series of excellent portrait parsing models (with only approximately 0.3M parameters) is received. Furthermore, we directly transfer the architectures searched on CelebAMask-HQ (Portrait Parsing) to other portrait and face segmentation tasks. Finally, we achieve the state-of-the-art performance of 96.5% MIOU on EG1800 (portrait segmentation) and 91.6% overall F1 -score on HELEN (face labeling). That is, our models significantly surpass the artificial network on the accuracy, but with lower resource consumption and higher real-time performance.

This article is concerned with the exponential synchronization of coupled memristive neural networks (CMNNs) with multiple mismatched parameters and topology-based probability impulsive mechanism (TPIM) on time scales. To begin with, a novel model is designed by taking into account three types of mismatched parameters, including: 1) mismatched dimensions; 2) mismatched connection weights; and 3) mismatched time-varying delays. Then, the method of auxiliary-state variables is adopted to deal with the novel model, which implies that the presented novel model can not only use any isolated system (regard as a node) in the coupled system to synchronize the states of CMNNs but also can use an external node, that is, not affiliated to the coupled system to synchronize the states of CMNNs. Moreover, the TPIM is first proposed to efficiently schedule information transmission over the network, possibly subject to a series of nonideal factors. The novel control protocol is more robust against these nonideal factors than the traditional impulsive control mechanism. By means of the Lyapunov-Krasovskii functional, robust analysis approach, and some inequality processing techniques, exponential synchronization conditions unifying the continuous-time and discrete-time systems are derived on the framework of time scales. Finally, a numerical example is provided to illustrate the effectiveness of the main results.

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

In this work, a novel dynamic-memory event-triggered$H_{\infty }$load frequency control (LFC) approach for the power system is proposed considering the existence of hybrid attacks. A dynamic-memory event-triggered mechanism (DMETM) is first presented under denial-of-service (DoS) attacks to reduce the occupation of network communication bandwidth. Different from the existing event-triggered mechanisms (ETMs), the superiority of DMETM is that not only the past transmitted packets can be utilized but also the amount of utilized packets can be adjusted according to the state error of the power system. Then, the general LFC model of the power system is reconstructed as a switched system on account of the existence of DoS attacks and deception attacks. Based on the reconstructed switched model, an exponentially mean-square stability criterion with an$H_{\infty }$performance index is derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). Furthermore, the DMETM controllers and event-triggered weighting matrices can be obtained by solving the relevant linear matrix inequalities (LMIs). Finally, some illustrated examples are presented to demonstrate the feasibility and effectiveness of the approach proposed.

This article analyzes the mechanism of denial-of-service (DoS) attacks initiated by hackers from the perspective of computer networks. In order to effectively estimate the performance error generated by the T–S fuzzy networked control systems under DoS attacks, we transform the performance error estimation problem into the one of finding ellipsoid constraints$\mathfrak {J}(P_{i})$. First, improved Lyapunov–Krasovskii functions (LKFs) based on fuzzy membership functions are constructed, which combine the characteristics of nonlinear problems in the system to reduce the initial constraints. Then, a second-order weight method (SOWM) is introduced to divide the time interval of sampling meticulously. Besides, the information stored in the LKFs is enhanced. Furthermore, we construct the novel looped functions by relying on the SOWM. Next, a suitable integral elastic event trigger mechanism is established to ensure that the performance errors caused by the attacks are estimated. Finally, the feasibility of the proposed method is verified by a two-degree-of-freedom helicopter system.

In this article, we focus on the event-triggered observer-based fuzzy positioning control and$L_{2}$-gain analysis of nonlinear unmanned marine vehicle (UMV) systems in network environments, which is subject to a hybrid attack consisting of denial-of-service attack and deception attack. First, based on the Tagaki-Sugeno fuzzy modeling method, a unified switched system model is established for the positioning control of nonlinear UMV systems with actuator saturation, multiplicative gain uncertainties, external disturbance, and hybrid attacks. Then, by resorting to introducing the integral-type state error, a novel adaptive memory-based event-triggered mechanism is proposed to lighten the communication load in UMV systems. Furthermore, a sufficient criterion is proposed to make the mean square exponential stability of UMV systems with a nonweighted$L_{2}$-gain level be guaranteed, and gain parameters of the designed controller and observer are also solved efficiently. Finally, the simulation example demonstrates the effectiveness of the proposed nonfragile control strategy.

This article proposes an improved security-based event-triggered fuzzy control (ETFC) method for studying the asymptotic stability problem of nonlinear networked control systems (NCSs) under denial-of-service (DoS) attacks. The nonlinear NCSs are linearized using the central mean fuzzy method to obtain Takagi-Sugeno (T-S) fuzzy NCSs. Lyapunov–Krasovskii functionals (LKFs) with nonlinear problems are constructed based on fuzzy membership functions. A relaxed condition including nonlinear parameters is given, which considers the nonlinear optimization problem and reduces the positive-definiteness constraint problem of LKFs. An improved double-closed delay correlation function is constructed to obtain more sampling information. Furthermore, a quadratic scaling method is used to obtain a tighter upper bound, making the criterion less conservative. A DoS attack causes the channel's throughput to drop and collapse. Thus, in this article, we design ETFC based on the improved descent gradient algorithm to ensure the communication security of the helicopter system (HS). Finally, the communication security and stability of HS are verified using the Simulink platform.

This article focuses on the membership-mismatched impulsive exponential stabilization for fuzzy unconstrained multilayer neural networks (MNNs) with node-dependent time-varying delays (NDTVDs). To begin with, this work proposes a novel MNNs model with unconstrained interlayer and intralayer parameters, which may allow nodes in all layers to have inconsistent attributes and structures. Meanwhile, the novel model considers the NDTVDs and removes the strict constraints including node alignment and one-to-one interlayer connection to meet diverse modeling requirements in complex applications. Then, the proposed fuzzy impulsive controller does not need to share the same fuzzy parameters as the fuzzy MNNs model, reducing the implementation complexity of the fuzzy impulsive controller. To derive the main results using the augmented vector form of unconstrained MNNs, the sparse matrix method is proposed to convert the node-dependent delayed MNNs model into an equivalent model with multiple delays. Moreover, the time-dependent Lyapunov function (TDLF) technique is adopted to improve the reliability of the stabilization conditions by fully utilizing the state information of both the current and neighboring impulsive intervals. Finally, the main results are verified using numerical simulation.

The constrained Takagi–Sugeno fuzzy systems with local nonlinear models (N-TS fuzzy systems) are much more practical than the conventional T–S fuzzy models, but their applications are seriously affected by either the great conservatism or the heavy computational burden. In this short article, an enhanced local stabilization of constrained N-TS fuzzy systems is investigated with the purpose of not only reducing conservatism but also relieving computational burden. To do this, a new class of threshold functions are established in order to achieve real-time online assessment of the variation of the normalized fuzzy weighting functions (NFWFs) at different sampling instants, i.e., the one-step-past one and the current one. Thus, a key alternative is proposed for eliminating all the involved terms containing the one-step-past NFWFs while their main information is retained. Since our obtained design criteria are merely dependent on the current NFWFs, the conservatism can be reduced at the expense of lighter computational burden. The benefits of our proposed results are validated by implementing several objective comparisons with those recent results.

This article investigates the finite-time secure filter design of fuzzy switched cyber-physical systems equipped with a resource-constraint network that may undergo false data injection attacks (FDIAs). To strike a higher level balance between the resource consumption and filtering performance, a multidomain probabilistic event-triggered mechanism (MDPETM) is initially developed. And the mode mismatched phenomenon between the filter and the system is characterized through a delayed switching signal. Based on the MDPETM and a virtual delay partitioning approach, fuzzy mismatched secure filters are first devised whose modes could differ from the system. Then, filter-mode-dependent Lyapunov functionals are created to obtain new sufficient criteria such that the filtering error achieves finite-time boundedness with extended dissipativity subject to admissible FDIAs. The filter gains are obtained by solving a set of convex optimization problems. Finally, an application-oriented example is employed to test the effectiveness and advantages of the proposed results.

A distributed coordination load frequency control (LFC) approach is proposed for interconnected power systems, and a detection and compensation mechanism targeting denial-of-service (DoS) attacks is designed in this work. Firstly, different from traditional LFC, a hybrid-type distributed control strategy is proposed, which takes full advantage of local control information and coordination control information. Then, for discovering DoS attacks timely, a feasible detection algorithm targeting DoS attacks is presented. Furthermore, in order to weaken the harm of cyber attacks, a triggered-type compensation mechanism for control data is proposed. By constructing appropriate Lyapunov–Krasovskii functionals, a stability criterion with an$H_{\infty }$performance index is developed. At last, the feasibility of detection and compensation mechanism targeting DoS attacks and the effectiveness of proposed control approach are demonstrated by presenting contrastive illustrative examples.

This article is dedicated to investigating the impulsive-based almost surely synchronization issue of neural network systems (NSSs) with quality-of-service constraints. First, the communication network considered suffers from random double deception attacks, which are modeled as a nonlinear function and a desynchronizing impulse sequence, respectively. Meanwhile, the impulsive instants and impulsive gains are randomly and only their expectations are available. Second, by taking two different types of random deception attacks into consideration, a novel mathematical model for vulnerable NSSs is constructed. Then, almost surely synchronization criteria are established by using Borel-Cantelli lemma. Furthermore, based on the derived strong and weak sufficient conditions, the almost surely synchronization of NSSs is achieved. Finally, the section of numerical example is shown to illustrate the effectiveness of the proposed method.

As edge computing platforms need low power consumption and small volume circuit with artificial intelligence (AI), we design a compact and stable memristive visual geometry group (MVGG) neural network for image classification. According to characteristics of matrix–vector multiplication (MVM) using memristor crossbars, we design three pruning methods named row pruning, column pruning, and parameter distribution pruning. With a loss of only 0.41% of the classification accuracy, a pruning rate of 36.87% is obtained. In the MVGG circuit, both the batch normalization (BN) layers and dropout layers are combined into the memristive convolutional computing layer for decreasing the computing amount of the memristive neural network. In order to further reduce the influence of multistate conductance of memristors on classification accuracy of MVGG circuit, the layer optimization circuit and the channel optimization circuit are designed in this article. The theoretical analysis shows that the introduction of the optimized methods can greatly reduce the impact of the multistate conductance of memristors on the classification accuracy of MVGG circuits. Circuit simulation experiments show that, for the layer-optimized MVGG circuit, when the number of multistate conductance of memristors is$2^{5}= 32$, the optimized circuit can basically achieve an accuracy of the full-precision MVGG. For the channel-optimized MVGG circuit, when the number of multistate conductance of memristors is$2^{2}= 4$, the optimized circuit can basically achieve an accuracy of the full-precision MVGG.

This article investigates the approximate optimal control problem for nonlinear affine systems under the periodic event triggered control (PETC) strategy. In terms of optimal control, a theoretical comparison of continuous control, traditional event-based control (ETC), and PETC from the perspective of stability convergence, concluding that PETC does not significantly affect the convergence rate than ETC. It is the first time to present PETC for optimal control target of nonlinear systems. A critic network is introduced to approximate the optimal value function based on the idea of reinforcement learning (RL). It is proven that the discrete updating time series from PETC can also be utilized to determine the updating time of the learning network. In this way, the gradient-based weight estimation for continuous systems is developed in discrete form. Then, the uniformly ultimately bounded (UUB) condition of controlled systems is analyzed to ensure the stability of the designed method. Finally, two illustrative examples are given to show the effectiveness of the method.

In the cooperative control for multiagent systems (MASs), the key issues of distributed interaction, nonlinear characteristics, and optimization should be considered simultaneously, which, however, remain intractable theoretically even to this day. Considering these factors, this article investigates leader-to-formation control and optimization for nonlinear MASs using a learning-based method. Under time-varying switching topology, a fully distributed state observer based on neural networks is designed to reconstruct the dynamics and the state trajectory of the leader signal with arbitrary precision under jointly connected topology assumption. Benefitted from the observers, formation for MASs under switching topologies is transformed into tracking control for each subsystem with continuous state generated by the observers. An augmented system with discounted infinite LQR performance index is considered to optimize the control effect. Due to the complexity of solving the Hamilton-Jacobi-Bellman equation, the optimal value function is approximated by a critic network via the integral reinforcement learning method without the knowledge of drift dynamics. Meanwhile, an actor network is also presented to assure stability. The tracking errors and estimation weighted matrices are proven to be uniformly ultimately bounded. Finally, two illustrative examples are given to show the effectiveness of this method.

This article investigates the problem of relaxed exponential stabilization for coupled memristive neural networks (CMNNs) with connection fault and multiple delays via an optimized elastic event-triggered mechanism (OEEM). The connection fault of the two or some nodes can result in the connection fault of other nodes and cause iterative faults in the CMNNs. Therefore, the method of backup resources is considered to improve the fault-tolerant capability and survivability of the CMNNs. In order to improve the robustness of the event-triggered mechanism and enhance the ability of the event-triggered mechanism to process noise signals, the time-varying bounded noise threshold matrices, time-varying decreased exponential threshold functions, and adaptive functions are simultaneously introduced to design the OEEM. In addition, the appropriate Lyapunov-Krasovskii functionals (LKFs) with some improved delay-product-type terms are constructed, and the relaxed exponential stabilization and globally uniformly ultimately bounded (GUUB) conditions are derived for the CMNNs with connection fault and multiple delays by means of some inequality processing techniques. Finally, two numerical examples are provided to illustrate the effectiveness of the results.

This article focuses on the design of a mode- dependent adaptive event-triggered control (AETC) scheme for the stabilization of Markovian memristor-based reaction-diffusion neural networks (RDNNs). Different from the existing works with completely known transition probabilities, partly unknown transition probabilities (PUTPs) are considered here. The switching conditions and values of memristive connection weights are all correlated with Markovian jumping. A mode-dependent AETC scheme is newly proposed, in which different adaptive event-triggered mechanisms will be applied for different Markovian jumping modes and memristor switching modes. For each given mode, the corresponding event-triggered mechanism can efficiently reduce the number of transmission signals by adaptively adjusting the threshold. Thus, the mode-dependent AETC scheme can effectively save the limited network communication resources for the considered system. Based on the proposed control scheme, a new stabilization criterion is set up for Markovian memristor-based RDNNs with PUTPs. Meanwhile, a memristor-dependent AETC scheme is devised for memristor-based RDNNs. Finally, simulation results are presented to verify the effectiveness and superiority of the analysis results.

This article investigates the stability problem of Takagi–Sugeno fuzzy networked control systems (TSNCSs) under deception cyberattacks via a reliable sampling mechanism, which has important research value for applications in network security. First, a fuzzy weight functional method is introduced, and a new Lyapunov–Krasovskii functional is developed, which better incorporates nonlinear problems in the model. Then, in order to reduce the initial constraints, improved time delay closed-loop functions are constructed that consider the delay information and the characteristics of the sampling time points. Furthermore, considering the reliability issues of controllers in real industry, we establish some sufficient conditions and implement a novel reliable sampling controller with deception attacks (DAs) to control the asymptotic stability of TSNCS. Finally, the correctness and feasibility of the proposed method are verified experimentally using an inverted pendulum system.

This article investigates the exponential synchronization problem for a class of memristor-based neural networks with mixed time-varying delays and parameter perturbations, and inertial items are considered (MINNs). A periodically intermittent control protocol is designed to guarantee the exponential synchronization between two MINNs. Then, by adopting nonsmooth analysis, Halanay inequality, and Lyapunov theory, the exponential synchronization criteria for MINNs under the proposed controller are obtained. Furthermore, instead of the reduced-order method, the synchronization of MINNs is considered under the framework of the second-order system directly, which is different from existing literature. Some numerical simulations are presented to show the validity of the proposed criteria in the end.

Safety is always one of the most critical principles for a control system. This article investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefiting from the dynamic regressor extension and mixing (DREM), an extended elementwise parameter identification law is utilized to dismiss the uncertainty. It is shown that the proposed control scheme can always ensure safety in the identification process with injected excitation noise. Besides, the elementwise identification process using DREM can minimize the theoretical conservatism of the safe adaptation law compared to other existing adaptive CBF (aCBF) algorithms. The stability of the proposed safe control scheme is proven, where the safety is guaranteed by constructing appropriate forward invariant aCBF. Furthermore, the robustness of our algorithms under bounded disturbances is analyzed. Finally, the proposed framework is tested on two simulation-based examples, including the adaptive cruise control problem where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances, and the potential crash risk is avoided by our safe control scheme. These examples illustrate the effectiveness of our algorithm.

This article presents an innovative efficient safety-critical control scheme for nonlinear systems by combining techniques of control barrier function (CBF) and online time-varying optimization. The idea lies in that when directly calling the complete optimization solvers used in the CBF method, such as CBF-based quadratic programming (CBF-QP), is computationally inefficient for complex tasks, the suboptimal solutions obtained from online learning techniques can be an alternatively reliable choice for ensuring both efficiency and control performance. By using the barrier-based interior point method, the original optimization problem with CBF constraints is reduced to an unconstrained one with approximate optimality. Then, Newton- and gradient-based continuous dynamics are introduced to generate alternative cheap solutions while ensuring safety. By further considering the lag effect of online tracking, a prediction term is added to the dynamics. In this way, the online cheap solutions are proven to exponentially converge to the time-varying suboptimal solutions of the interior point method. Furthermore, the safety criteria are established, and the robustness of the designed algorithms is analyzed theoretically. Finally, the effectiveness is illustrated by conducting two experiments on obstacle avoidance and anti-swing tasks.

In this article, the consensus problem of nonlinear multiagent systems (MASs) with time-varying communication delays (TVCDs) is addressed by proposing a novel nonfragile memory-based sampled-data control approach. Different from the existing sampled-data control schemes, an improved memory-based proportional derivative like a sampled-data control scheme based on the information of neighbors is proposed. Besides, considering the inaccuracy and perturbation of controller, a nonfragile control method is also adopted to design the controller. Then, instead of the general quadratic type of the Lyapunov–Krasovskii functional, a delay-product-type quadratic is presented by taking full advantage of the information of TVCDs, and the positive definite constraint in the entire time domain is weakened to the sampled instants on account of adopting the two-sided looped-functional approach. Furthermore, less-conservative consensus criteria for MASs are developed. By solving linear matrix inequalities, the desired control gain matrices and larger sampled-data periods are achieved. Finally, illustrative examples are employed to demonstrate the effectiveness of the designed control strategy.

The asymptotical synchronization in mean square of reaction–diffusion neural networks (RDNNs) with random delays is studied in this article. By sampling on both the time domain and spatial domain, a time–space sampled-data controller (TSSDC) is designed, which can efficiently save the network communication resources for RDNNs. A new processing method for the TSSDC is provided. Compared with the existing methods, the processing method here can capture more sampling information and is more concise. An extended Poincaré–Wirtinger inequality is proposed, which is in matrix form and less conservative. Then by constructing a sampling-dependent LKF, using the extended Poincaré–Wirtinger inequality and Hölder inequality, new mean square asymptotical synchronization criteria are set up for RDNNs with random delays, and the desired TSSDC gain is obtained. At length, a numerical example is given to verify the effectiveness and superiority of the obtained results.

This article investigates the privacy-preserving average consensus problem in multiagent systems. A new approach is proposed to achieve the accurate average consensus value while protecting the initial state information of agents. The key idea to achieve these goals is based on partial information transmission. Each agent decomposes its initial state information into several different subinformation. The values of these subinformation are chosen randomly but with their mean value fixed to the original initial value. Then, based on multiple communication channels, agents share all their subinformation, but ensure that each neighbor can only obtain partial subinformation. We prove that the privacy can be protected under our method if each agent has at least two neighbors, and one of the neighbors is neutral. The generalization on the directed graph about this method is also considered. Finally, two examples are provided to illustrate the design process and practical applications of the proposed approach.

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems is addressed by using reinforcement learning. In contrast to traditional reinforcement learning-based methods for optimal coordination and control of multi-agent systems with a time-triggered control mechanism, an event-triggered mechanism is proposed to update the controller only when the designed events are triggered, which reduces the computational burden and transmission load. The stability analysis of the closed-loop multi-agent systems with event-triggered controller is described. Further, to implement the proposed scheme, an actor-critic neural network learning structure is proposed to approximate performance indices and to on-line learn the event-triggered optimal control. During the training process, event-triggered weight tuning law has been designed, wherein the weight parameters of the actor neural networks are adjusted only during triggering instances compared with traditional methods with fixed updating periods. Further, a convergence analysis of the actor-critic neural network is provided via Lyapunov method. Finally, two simulation examples show the effectiveness and performance of the obtained event-triggered reinforcement learning controller.

In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the$H_{\infty }$attenuation index and$H_{\_{}}$increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly$(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$-dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme.

This article addresses the problem of dynamic pinning synchronization of fuzzy-dependent-switched (Fds) coupled memristive neural networks (CMNNs) with mismatched dimensions on time scales. To begin with, the probabilistic coupling delays, time scales, mismatched dimensions, and function projective synchronization rules are considered to design the novel CMNNs to improve the reliability and generalization ability of the model. Then Fds rules and dynamic pinning control (DPC) method are adopted to design the CMNNs, which can effectively promote the information exchange between the switching signals and the fuzzy processes and can improve the utilization of the communication bandwidth between the nodes of CMNNs. Meanwhile, the method of constructing auxiliary state variables is adopted here to deal with the presented model, so that the coupled and isolated systems with different dimensions can realize information exchange and data sharing. This method also provides a solution for researchers by using low-dimensional systems to estimate or synchronize high-dimensional systems. Moreover, by means of Lyapunov–Krasovskii functional, auxiliary orthogonal matrix, and some inequality processing techniques, the conditions of modified function projective synchronization for Fds CMNNs are derived via the DPC on time scales. Finally, two numerical examples are provided to illustrate the effectiveness of the main results.

This article investigates the heterogeneous impulsive synchronization for T-S fuzzy probabilistic coupled delayed neural networks (CDNNs) with mode-dependent parameters and piecewise membership functions. To begin with, a novel CDNNs model with adjustable coupling strength and probabilistic coupling delays is designed to ensure the accuracy of the CDNNs model. Meanwhile, the generalized isolated node with four types of mismatched parameters, named heterogeneous isolated delayed neural network, is first considered to extend the synchronization problem. Then, the mode-dependent fuzzy rules are introduced to design the novel model, which implies that switching signals and fuzzy processes are interdependent and can share information to communicate. To improve the hybrid controller’s reliability, the mode-dependent impulses are also developed here, in which the impulsive effects with different properties can occur at any moment in the switching interval. The exponential synchronization conditions are derived by means of the method of auxiliary state variables, Lyapunov–Krasovskii functional, average switching dwell period, and mode-dependent average impulsive dwell period. Moreover, the improved mode-dependent piecewise approximated membership functions are proposed to reduce the main results’ conservatism. Finally, a numerical example is provided to illustrate the effectiveness of the main results.

In this article, a general approach to fixed-time synchronization problem is investigated for the general system of fractional-order multidimension-valued fuzzy memristive neural networks. First, we complete the establishment of the new model which is so general that we can regard it as fractional-order real-valued fuzzy memristive neural networks, fractional-order complex-valued fuzzy memristive neural networks, and fractional-order quaternion-valued fuzzy memristive neural networks. Then, we mainly apply two new general inequalities such as extended Cauchy–Schwarz inequality and generalized derivative of fractional-order absolute value function in order to realize the general analysis on the discussed problem. Owing to the two new lemmas, we can construct the general Lyapunov–Krasovskii functional with adjustable coefficients, design the nonlinear controllers with fuzzy gains, as well as acquire the flexible criteria with several useful factors. Particularly, the acquisition of the less conservative fixed time benefits from the new controllers which not only contains the common feedback gains but also can be comprised of the general coefficients and the fuzzy gains. Finally, a numerical example is provided to demonstrate our theoretical results.

In this paper, relaxed observer-based state estimation of discrete-time Takagi–Sugeno fuzzy systems is proposed via developing a new augmented matrix approach. In contrast with the recent method reported in the literature, a more advanced introduction mode of additional matrices is developed without any redundant constraint under the framework of homogeneous polynomials. Then, the proposed augmented matrices can be built in order to depict the characteristic information of each possible working mode in a superior way, and thus our obtained result is less conservative than the recent one. Moreover, it is worth noting that that the recent method belongs to the special case of ours. Finally, two numerical simulations are given to test and validate the superiority and generality of the developed method.

Resilient fuzzy stabilization is capable of providing much less conservative results than conventional fuzzy stabilization while the alert threshold condition should be always satisfied at each sampling instant. In order to make the alert threshold condition more easily to be guaranteed, the short paper employs the switching-type gain-scheduling control law so that the real-time information, which is specific to the current sampling instant, can be integrated into resilient fuzzy stabilization. More importantly, a new kind of time-variant balanced matrix is introduced for the first time for adjusting positive/negative terms of different monomials in a more flexible way. As a result, the conservatism of resilient fuzzy stabilization can be further reduced even if the alert threshold condition becomes more difficult to be violated. Finally, the advantage of the developed method is tested and validated via related comparisons on a benchmark example.

In this article, a novel co- design approach of observed-based fault detection filter (FDF) and dynamic event-triggered controller is proposed for multi-area wind power system under dual alterable aperiodic (DAA) denial-of-service (DoS) attacks. It is the first attempt to design an observed-based FDF for wind power system considering actuator fault signal. And a tolerable actuator fault threshold function is constructed to warn the occurrence of fault signal. Then, considering the bandwidth limitation of network communication, a novel dynamic event-triggered scheme is proposed to decrease the occupation of communication channel. Furthermore, the DAA DoS attacks presented in this work have different blocking effects on data transmission in different network channel. On account of the existence of aperiodic DoS attacks, the general load frequency control model for wind power system is reconstructed as a new switched system. Based on the reconstructed model, the stability with an$H_{\infty }$performance index is demonstrated by constructing appropriate polynomial Lyapunov-Krasovskii functionals. Besides, the FDF and dynamic event-triggered controller can be achieved by solving linear matrix inequalities. Finally, some contradistinctive case studies are given to illustrate the feasibility and effectiveness of the approaches proposed in this article.

This study focuses on the novel dissipative sampled-data control mechanism for high-speed trains systems (HSTSs) with quantized measurements. Firstly, an improved time-delay-product function (TDPF) is introduced for developing a new Lyapunov-Krasovskii functional (LKF), which fully considers the delay information and the delay derivative information. Then, by combining LKF theory with new analysis techniques, relaxed conditions are established. Furthermore, the desired dissipative sampled-data controller with state quantization (DSDCSQ) is designed to ensure that HSTS is asymptotically stable under an optimizing performance index. Finally, the Japan Shinkansen high-speed trains (HSTs) example is given to illustrate the effectiveness.

In this article, a new event-triggered$H_{\infty }$load frequency control (LFC) approach with dynamic triggered algorithm (DTA) for multi-area nonlinear power systems (NPSs) based on non-fragile proportional integral control (NPI-control) strategy is addressed. Firstly, different from the existing linear single-area LFC model for power systems, an improved nonlinear multi-area model with the performance of large-scale adjustment frequency fluctuation is constructed by considering the phenomenon of overshoots and long-term oscillations. Due to the existence of control uncertainty, it is the first time that the NPI-control scheme is applied to LFC approach for NPSs. Then, the DTA is proposed to adjust the dynamic event-triggered parameters, which reduces the occupation of communication bandwidth and the data computation of NPSs. Furthermore, a modified quadratic form with time-varying matrix and two-side closed functional method are adopted to construct the relaxed Lyapunov-Krasovskii functional, where some slack matrices are unnecessarily positive definite. Based on Lyapunov method, some less-conservatism stability criteria are derived. Utilizing the linear matrix inequality toolbox, the allowable upper bound of time-varying delays and the NPI-controller are obtained. Finally, a numerical example is presented to demonstrate the availability of the approach developed in this work.

This article studies the observer-based adaptive synchronization of multiagent systems (MASs) with unknown parameters under attacks. First, to estimate the state of agents, the observer for MAS is introduced. When disturbance, nonlinear function, and system model uncertainty are not considered, the nominal controller is proposed to achieve synchronization and state estimation. Then, in order to eliminate the effect of unknown parameters in the disturbance, nonlinear function, and system model uncertainty, the adaptive controller with switching term is introduced. However, the attack will lead to the destruction of the network topology so as the destruction of the nominal controller. By constructing an appropriate Lyapunov function, we analyze the effect caused by attacks, and the security control law is given to make sure the synchronization of the MASs under attacks. Finally, a numerical simulation is given to verify the validness of the obtained theorem.

Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.

In the brief, delayed impulsive control is investigated for the synchronization of chaotic neural networks. In order to overcome the difficulty that the delays in impulsive control input can be flexible, we utilize the concept of average impulsive delay (AID). To be specific, we relax the restriction on the upper/lower bound of such delays, which is not well addressed in most existing results. Then, by using the methods of average impulsive interval (AII) and AID, we establish a Lyapunov-based relaxed condition for the synchronization of chaotic neural networks. It is shown that the time delay in impulsive control input may bring a synchronizing effect to the chaos synchronization. Furthermore, we use the method of linear matrix inequality (LMI) for designing average-delay impulsive control, in which the delays satisfy the AID condition. Finally, an illustrative example is given to show the validity of the derived results.

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis. Furthermore, a distributed online learning framework, namely, reinforce-critic-actor neural networks, is established in the implementation of the proposed approach, which is aimed at estimating the IRR function, the Q-function, and the optimal control scheme, respectively. The implemented procedure is designed in a data-driven way without needing knowledge of the system dynamics. Finally, simulations and comparison results with the classical method are given to demonstrate the effectiveness of the proposed tracking control method.

In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage$V_{t}$of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 28and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.

This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict (Υ1, Υ2, Υ3)-γ-dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua's circuit and quadruple-tank process system (QTPS).

The issue of finite-time stabilization (FTS) for the memristor-based inertial neural networks (MINNs) with mixed time-varying delays (MTVDs) is researched by virtue of a new analytical method in this brief. First, an appropriate reliable control strategy is proposed for MINNs, which takes the influence of actuator failures into account. Second, by combining Lyapunov functional theory with new analysis techniques, novel theoretical results to guarantee the FTS for the concerned MINNs are acquired, and the desired reliable controller gains are obtained simultaneously. In additions, compared with the previous research works, the FTS results obtained in this paper are established directly from the MINNs themselves without using variable transformation method. In the end, two simulations are exploited to show the correctness and practicability of the acquired theoretical results.

This paper proposes two kinds of algorithms to achieve privacy-preserving consensus of multi-agent systems over undirected graphs via node decomposition mechanism and homomorphic cryptography technique. Based on the number of neighboring nodes ( |\mathscr Ni|), every agent is decomposed into |\mathscr Ni| subagents, which are connected as a chain graph. Note that every subagent connects one and only one non-homologous subagent (generated by different agents). Information interaction between non-homologous subagents is encrypted by a homomorphic cryptography algorithm, and homologous subagents exchange information directly. In this regard, the proposed node decomposition mechanism enhances the privacy of the initial values without increasing the computational complexity of encryption. The first privacy-preserving algorithm can achieve the accurate average consensus, which means that the agreement value of every subagent is consistent with the original average consensus value. The second algorithm studies the privacy-preserving scaled consensus problem without a priori knowledge about the underlying graph. Although the final convergence values of subagents do not keep exactly the same, homologous subagents can compute the original group decision value by resorting to the product of the limit value and agent's degree. Importantly, this algorithm also guarantees the privacy of group decision value of the whole system. Besides, it is proved that the privacy of the initial value can be preserved if the agent has at least one neutral neighbor.

This article studies the nonfragile sampled-data filtering problems for Takagi-Sugeno fuzzy systems with uncertainties and delays. By introducing two delay-product-type terms and other augmented terms, a novel Lyapunov-Krasovskii functional containing more detailed information of time delays is constructed. With the help of some new bounding inequalities, improved stability results for the addressed systems are established. Besides, a resilient sampled-data controller is devised with less cost. Furthermore, the efficiency of the obtained criteria is illustrated by a numerical example.

This paper addresses the convergence analysis on the guaranteed robust H∞control for master-slave generalized uncertain neural networks (GUNNs). Synchronization problems are raised up due to the existence of the disturbance loading and parameter uncertainties. In order to cope with the encountered robustness issues, a dual geometric sequence division-dependent augmented Lyapunov-Krasovskii functional is newly constructed, which contains state variable-based integral forms with unfixed intervals. Meanwhile, the convex combination technique is employed to deal with not only the parameter uncertainties but also the derivative of delay τ(t). To ensure the GUNNs to be globally asymptotically stable with the guaranteed H∞performance in the case of disturbance and parameters uncertainties, a controller is designed using the liner matrix inequalities technique. Numerical examples show that, in the sense of the prescribed H∞ performance, this proposed work achieves expected results on the error synchronization system.