This work studies the decentralized sliding mode control problem for interconnected systems based on a dynamic event-triggered transmission mechanism. To model unreliable communication networks that are susceptible to both energy-limited denial-of-service (DoS) attacks and transmission failures, a Markov model is adopted. A decentralized dynamic event-triggered mechanism is introduced to efficiently utilize limited channel resources by regulating data transmission based on system measurement outputs. Moreover, a hidden Markov model is used to estimate the working operation of the network transmission channel such that a hidden Markov model-based sliding mode controller can be designed by using the output feedback control method. By constructing a Lyapunov function, sufficient conditions are deduced to ensure the stochastic stability of the system and the reachability of the specified sliding mode surface, and a solvable criterion for obtaining the hidden Markov model-based sliding mode controller gains is presented. Finally, simulation results of a four-area interconnected power system are presented to demonstrate the effectiveness of the proposed method.

This paper investigates the distributed event-triggered fixed-time bipartite average tracking problem for multi-agent systems(MASs) with nonlinear dynamics. Different from the finite-time algorithm, fixed-time consensus strategy is independent of the initial conditions of the system. Firstly, based on our proposed fixed-time bipartite consensus controller, the event-triggered mechanism is introduced to reduce the update frequency of the controller. Secondly, a controller with saturation function is designed to mitigate the chatting phenomenon of the control input. Drawing on the fixed-time control theorem and Lyapunov theory, the sufficient conditions for achieving fixed-time bipartite consensus under the event-triggered strategy are derived, and a strictly positive lower bound of the inter-event is provided to demonstrate that the Zeno behavior is avoided. Moreover, the fixed-time bipartite average tracking problem under denial-of-service (DoS) attacks is investigated, where the attack model is characterized by the average non-attack rate. Finally, to verify the validity of the theoretical results, numerical examples are presented for illustration.

This study presents a reinforcement learning-based interval type-2 (IT-2) fuzzy formation tracking control strategy for leader-follower flexible joint robots (FJRs), addressing challenges arising from nonlinearities, unmodeled dynamics, and unknown external disturbances. The proposed approach employs fractional-order sliding mode control (FOSMC) to guarantee finite-time convergence of the tracking error with rigorously proven stability. Unlike conventional backstepping methods, the IT-2 fuzzy FOSMC framework effectively addresses uncertainties and disturbances. By considering the memory effect of fractional calculus, a Hamilton-Jacobi-Bellman equation for the formation tracking dynamics is derived using an auxiliary system and equivalent transformation. A novel cost function based on the derivative of the formation tracking error is introduced, resulting in a discounted cost that accounts for the Laplacian matrix$\widehat{L}$under a performance constraint. The optimal cost function and formation sliding mode control policy are shown to be gradually approximable via policy iteration. To achieve intelligent near-optimal control, a reinforcement learning algorithm based on an actor-critic neural network architecture is implemented, enabling all agents to compensate for system uncertainties and maintain formation tracking with the leader. Lyapunov stability analysis confirms that all follower FJRs can track the leader using actor-critic update laws for neural network weights, with distributed tracking error designs ensuring convergence to optimal values. Simulation results validate the proposed approach, demonstrating reduced chattering, improved formation tracking, and enhanced robustness.

This study presents a novel reinforcement learning (RL)-based, predefined-time tracking fault-tolerant control (FTC) scheme with prescribed performance for handling unknown stochastic nonlinear systems (SNSs) operating in various environments with actuator faults. The scheme addresses multiple actuator fault types, including loss-in-effectiveness (LIE) and lock-in-place (LIP), within a unified theoretical framework in which some actuators may become partially or completely disabled. This framework learns the stochastic nonlinear dynamics and control behaviors of the system using fuzzy logic systems (FLSs) within an RL-based identifier-critic-actor (ICA) structure. By combining prescribed performance control with predefined-time control, the proposed controller achieves fault-tolerant tracking performance, guarantees that all signals are probabilistically bounded, and preserves the output within a specified range. Unlike traditional FTC methods, which depend on knowing the system model to handle LIP faults and bias, this method addresses LIP faults without requiring prior knowledge of the system and achieves different performance levels by utilizing an RL-based ICA to adjust the FLS weights. A practical example using a single-link robot arm driven by a brushed dc (BDC) motor demonstrates the effectiveness and improved performance of the developed FTC scheme.

This paper studies the event-triggered sliding mode control problem for interval type-2 fuzzy interconnected systems under hybrid cyberattacks modeled by Markov processes. Under the framework of an interval type-2 fuzzy model, the nonlinear relationship in the interconnected system is modeled and analyzed, and a fuzzy sliding mode controller is designed which can effectively resist hybrid cyberattacks. To rationally utilize limited channel resources, a decentralized event-triggered mechanism is used to reduce unnecessary information transmission. By constructing a Lyapunov function, some criteria are deduced to ensure that the closed-loop system is stochastically passive and the reachability of the designed sliding area is guaranteed. Finally, a four-area networked interconnected system is used to verify the effectiveness of the decentralized fuzzy sliding mode control strategy.

This study investigates the resilient event-triggered load frequency control (LFC) issue of islanded microgrids (MGs) subject to denial of service attacks (DoSAs) and unknown false data injection attacks (FDIAs) simultaneously. An augmented extended observer is established to estimate both unavailable system states and unknown FDIA signals. A resilient adaptive event-triggered (AET) scheme, incorporating DoSA detection, FDIA estimation, and dynamic threshold adjustment, is proposed to reduce network resource consumption while countering cyberattacks. Some sufficient criteria are derived to ensure the asymptotic stability of the augmented estimation error system withH∞performance. Additionally, the uniformly ultimate boundedness of the system is also guaranteed even under hybrid cyberattacks. Finally, simulation studies are used to illustrate the effectiveness of the proposed method.

The main objective of this study is to develop an intelligent, resilient event-triggered control method for fractional-order multiagent networked systems (FOMANSs) using reinforcement learning (RL) to address challenges resulting from unknown dynamics, actuator faults, and denial-of-service (DoS) attacks. First, the challenge of unknown system dynamics within their environment must be addressed to achieve desired system stability in the face of unknown dynamics or to optimize consensus in FOMANSs. To address this problem, an adaptive learning law is implemented to handle unknown nonlinear dynamics, parameterized by a neural network, which establishes weights for a fuzzy logic system utilized in cooperative tracking protocols. A novel distributed control policy facilitates signal sharing through RL among agents, reducing error variables through learning. Moreover, this study combines an RL algorithm with the sliding mode control strategy to optimize the parameterization of the distributed control protocol, thereby eliminating its constraints on initial conditions. Second, realizing that DoS attacks typically make the actuator signal inaccessible for distributed control protocols, an innovative intelligent dual-event-triggered control strategy is formulated to reduce the effects of DoS attacks. By coordinating nested event triggers across various channels, the distributed control input is protected from incorrect signals from DoS attacks, thus ensuring its resilience. To address this problem, an intelligent security dual-event-triggered control protocol guarantees Mittag-Leffler stability of the closed-loop system and ensures effective sliding motion conditions. This distributed control protocol ensures robust tracking of control tasks and mitigates "Zeno behavior" during event triggering. The proposed control strategy is validated using a single-link flexible-joint robotic manipulator system.

This article proposes a new result on mismatched double fuzzy summation inequality. The mismatched double fuzzy summation inequality usually originates from the control system design with using the Takagi–Sugeno (TS) fuzzy model, in which the controller and the system are with mismatched membership functions. Compared with the existing method to deal with the mismatched double fuzzy summation inequality, the proposed one is less conservative and does not introduce additional decision variables. Three examples including the mismatched membership functions of the TS fuzzy control system in the discrete-time case, the continuous-time case and the interval type-2 fuzzy system under state feedback control mechanism are used to show that the new result is less conservative than the existing one.

This paper addresses the secure control problem for the Takagi-Sugeno (T-S) fuzzy wind turbine system (WTS) subject to hybrid cyberattacks. To reduce system performance loss and redundant data transmission under such attacks, a novel adaptive memory event-triggered mechanism (ETM) is proposed, offering two key advantages. First, unlike traditional ETMs that rely solely on current system information, the adaptive memory ETM utilizes historical release data to adjust communication frequency and enhance control effectiveness. Second, an attack-related triggering condition and adaptive law are introduced to reduce the invalid data transmission induced by denial of service (DoS) attacks and extend the lifespan of the sensor node. Sufficient conditions are derived to ensure the mean-square$\mathcal {H}_{\infty }$asymptotic stability of the resulting T-S fuzzy WTS, while also guaranteeing uniformly ultimate boundedness in the presence of DoS attacks. Finally, an illustrative example is used to show the effectiveness of the proposed method.

This article is devoted to studying the homogeneous polynomial parameter-dependent (HPPD)-type control strategy of active vehicle suspension systems (AVSSs) within a cyber-physical architecture that is subjected to malicious attacks. A framework based on cyber-physical systems is proposed for designing the controller parameters of an AVSS, considering the vulnerability of control signals in the transport layer to malicious cyberattacks. To address the hybrid attacks consisting of denial of service and false data injection (FDI), a resilient controller with FDI attack compensation is designed, and an augmented error system model under hybrid attacks is established. Subsequently, to further enhance handling stability and ride comfort, a homogeneous polynomial method is introduced, and a novel HPPD-type resilient controller with FDI attack compensation is devised. The co-design method for the HPPD-type controller and FDI attack observer is derived using a time-varying Lyapunov function to ensure the exponential stability of the HPPD-type augmented error system. Finally, the effectiveness of the proposed method in enhancing stability and improving comfort is verified through software simulations and hardware-in-the-loop experiments, and the superiority of the method in resisting attacks is demonstrated through comparative analysis of some performance indicators.

Enhancing lateral stability and driver comfort in the presence of driver behavior uncertainties is essential in the context of shared control for autonomous vehicles. In view of the absence of exact model based information in real time, this study harnesses the inverse reinforcement learning (IRL) procedure to establish the reward function for the automation model using expert data. In contrast to existing shared control studies that focus on automation counteracting driver behavior uncertainties, the novelty of the proposed study lies in developing human-like behavior within the shared control environment. Additionally, to achieve the overall objective of human like driving, RL based approach is employed to generate the automation road steer angle and driver automation (DA) relative weights, ensuring fulfillment of lane-keeping, vehicle lateral stability, and driver comfort objectives simultaneously. The reward function formulated for generating the DA relative weights and the automation model is integrated with the human arm muscular characteristics of the driver behavior model in the RL framework to develop the optimal shared steer angle. Comprehensive evaluations were performed to compare the driving performance of the suggested RL-based shared control system with existing adaptive shared control methods. Simulation outcomes indicate that the proposed control technique outperforms others by closely replicating human driving behavior. Additionally, a hardware-in-loop (HIL) setup was employed to validate the proposed shared control scheme under varying longitudinal speeds.

This article first introduces a sampled-data state estimator design method for continuous-time long short-term memory (LSTM) neural networks with irregularly sampled output. To this end, the structure of the LSTM is addressed to obtain its dynamic equation. As a result, the LSTM neural network is modeled as a continuous-time linear parameter-varying system that is dependent on the gate units. For this system, the sampled-data Luenberger- and Arcak-type state estimator design methods are presented in terms of linear matrix inequalities (LMIs) by using the properties of the gate units. Lastly, the proposed method not only provides a numerical example for analyzing absolute stability but also demonstrates it in practice by applying a pre-trained behavior generation model of a robot manipulator.

In this article, the interval type-3 fuzzy-based state feedback control is proposed for the stabilization problem of interval type-3 fuzzy systems (IT3FSs) subject to time-varying delay. Specifically, to improve the model accuracy and control capabilities, we proposed a nonparallel distributed compensation-based controller approach, be precise, the system and controller adopts the distinct membership functions. Moreover, the stability analysis of IT3FSs in the state space form is studied newly in this article. Besides the primary membership function that exists for interval type-2 fuzzy systems (IT2FSs), the IT3FSs possess the secondary membership function to handle the overall uncertainty which enacts the superiority of the proposed work. Notably, the adequate stability criteria are derived in the form of linear matrix inequality (LMI) using the Lyapunov stability theorem. Additionally, the convex relaxation technique is used to strengthen the design flexibility and achieve the faster convergence rate, which converts an optimization problem with a vector variable to a convex program with a matrix variable, via a lifting technique. A noteworthy aspect is that the application of the convex relaxation technique in the context of IT3FSs is proposed for the first time in literature. Finally, the comparative results between IT2FSs and IT3FSs are shown via two illustrative examples, including the inverted-pendulum model, to underscore the efficacy and practical implementations of the developed control methodology.

This paper focuses on the observer-based event-triggered(ET) scheme issue for interval type-2(IT-2) fuzzy systems under the multiple cyber attacks including false data injection (FDI) attacks and denial-of-service (DoS) attacks. Considering both intermittency of DoS attacks and unmeasurable of partial systems, a switched observer-based nonlinear systems in the form of interval type-2 (IT-2) fuzzy models is established. An adaptive event-triggered (AET) scheme that can modulate the threshold value is proposed for better saving the communication resources. Considering the impact of stochastic FDI attacks, a switched AET controller is designed. Then, Lyapunoval analysis method is developed such that the augmented error IT-2 fuzzy systems is mean square exponentially stable with$H_\infty$performance. Finally, a practical example is employed to reflect the validity of the approach.

This article concentrates on the sampled-data control problem by utilizing a novel mismatch parameter-dependent stabilization method for the Takagi–Sugeno (T-S) fuzzy system. First, taking the information on sampled-parameter and fuzzy weighted function into account, a novel sampled-parameter-dependent fuzzy Lyapunov function is proposed. Furthermore, an affine matched sampled-data controller is designed to contain an affine transformed membership function, thereby achieving larger stabilizable regions. Based on a novel fuzzy Lyapunov function and parameterized matrices bounding technique, a sufficient condition concerning the asymptotic stability of the closed-loop fuzzy system is formulated in the form of linear matrix inequality. In comparison with the prior works, the derived condition has less conservative and the largest sampling interval. Numerical experiments on Rosser's system confirm the advantages and benefits of the novel mismatch parameter-dependent stabilization approach.

This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.

In this article, an iterative cost-learning model predictive control (ICLMPC) is proposed for nonlinear networked control systems (NCSs) in the presence of aperiodic sampling. The proposed ICLMPC is useful not only to guarantee asymptotic stability of the closed-loop system with aperiodic sampling but also to improve control performance in the case of performing an iterative task. In the proposed method, the nonlinear system of NCSs is mathematically represented as an aperiodic sampled-data Takagi–Sugeno (T–S) fuzzy system. Based on this representation, the ICLMPC design is formulated in terms of a finite-horizon optimal control problem in which a new terminal cost function is considered. The terminal cost function is constructed by a Lyapunov function with a looped-functional and an iteratively minimized function (IMF). From the Lyapunov function with the looped-functional, it is possible to guarantee that the ICLMPC asymptotically stabilizes the aperiodic sampled-data T–S fuzzy system. To obtain an iteratively improved control performance, the IMF takes the minimized value among the integrals of the collected data at each iteration. The validity and effectiveness of the proposed method are illustrated by two practical examples in the simulation section.

Unsupervised anomaly detection is widely studied in industrial applications where anomalous data is difficult to obtain. In particular, reconstruction-based anomaly detection can be a feasible solution if there is no option to use external knowledge, such as extra datasets or pre-trained models. However, reconstruction-based methods have limited utility due to poor detection performance. A score-based model, also known as a denoising diffusion model, recently has shown a high sample quality in the generation task. In this paper, we propose a novel unsupervised anomaly detection method leveraging the score-based model. The proposed method shows promising performance without requiring external knowledge. The score, a gradient of the log-likelihood, has a property that is available for anomaly detection. The samples on the data manifold can be restored instantly by the score, even if they are randomly perturbed. We call this score-based perturbation resilience. On the other hand, the samples that deviate from the manifold cannot be restored in the same way. The variation of resilience depending on the sample position can be an indicator to discriminate anomalies. We derive this statement from a geometric perspective. Our method shows superior performance on three benchmark datasets for industrial anomaly detection. Specifically, on MVTec AD, we achieve image-level AUROC of 97.7% and pixel-level AUROC of 97.4% outperforming previous works that do not use external knowledge.

This article addresses the issue of input–output finite-time stabilization for interval type-2 fuzzy systems in the presence of deception attack effects. Our main goal is to make efficient use of network resources by developing an event-triggered controller for interval type-2 fuzzy systems. For the stabilization process, an event-triggered controller that does not share the same membership functions as the system is designed by using affine transformation parameters. Following that, by using an asymmetric Lyapunov–Krasovskii functional and some advanced integral inequalities to establish the sufficient conditions for the existence of the proposed controller. Furthermore, an asymptotic stabilization result is presented and discussed in a comparative analysis as a special case. Due to the asymmetric Lyapunov–Krasovskii functional structure, the transmission delay interval is large compared with some recent studies. Finally, two simulation examples are carried out and the efficiency of designed controller is verified.

This article presents a new sampled-data fuzzy filter design method for Takagi–Sugeno fuzzy systems with the asynchronous premise variables. In the new fuzzy filter design method, the membership functions of the filter are affine transformed by scaling and biasing the system’s membership functions. Taking the advantage of the newly proposed method, the asynchronous problem of the premise variables between the system and filter is easily resolved. Based on the looped function and a modified free-weighting matrix inequality, the sampled system’s output is handled, and the filter design condition is formulated in terms of a parameterized linear matrix inequality with affine matched fuzzy parameter vectors. Additionally, a modified Finsler’s lemma is devised to handle the affine matched fuzzy parameter vectors. By utilizing the relationship between the transformed membership functions, the filter design condition for$H_{\infty }$performance is enhanced with larger allowable maximum bounds of variable sampling intervals. Lastly, the superiority of the presented method is verified by comparing the numerical simulations with existing methods.

This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the$H_{\infty }$state estimation performance of the static neural networks with time-varying delay. The nonlinearity of the activation function has a significant impact on stability analysis and robustness/performance. In the proposed state estimator, a parameter-dependent estimator gain is reconstructed by using the properties of the sector nonlinearity of the activation functions that are represented as linear combinations of weighting parameters. In the reformulated form, the constraints of the parameters for the activation function are considered in terms of linear matrix inequalities. Based on the Lyapunov–Krasovskii function and the improved reciprocally convex inequality, enhanced conditions for designing a new state estimator that guarantees$H_{\infty }$performance are derived through a parameterization technique. The compared results with recent studies demonstrate the superiority and effectiveness of the presented method.

Stabilization of type-2 fuzzy system in the presence of cyber attacks is investigated in this article. For a practical application, a class of nonlinear system can be represented by an interval type-2 fuzzy system through a set of membership functions. Unlike existing schemes, 1) affine membership functions are considered in the controller design; moreover, 2) a robust adaptive event-triggered control is proposed to avoid the unwanted triggering events, which makes the proposed scheme more reliable and relaxes the conservativeness of stability analysis.In the numerical simulation, the mass-spring-damper system and the tracking control system are considered to illustrate the robustness and effectiveness of the proposed approach.

This paper presents a new parallel distributed compensation controller design approach for T–S (Takagi–Sugeno) fuzzy control systems with affine matched membership functions in the system and controller. In the new fuzzy control, affine transformed membership functions are adopted by scaling and biasing the original membership functions of the system. Stabilization and performance criterion of the closed-loop T–S fuzzy systems are obtained through a new parameterized linear matrix inequality, which is rearranged by affine matched membership functions. The conservativeness of stabilization condition for the T–S fuzzy system is significantly relaxed by utilizing the constraints condition of the controllers membership functions, which is determined from the difference of each transformed membership function. In addition, the controller gain is reconstructed by a decision variable separation technique with two different free weighting matrices without any scaling parameter. The superiority of proposed method is verified through numerical examples.

In this paper, event-triggered reliable dissipative filtering is investigated for a class of Takagi-Sugeno (T-S) fuzzy systems. First, a reliable event-triggered communication scheme is introduced to release sampled measurement outputs only if the variation of the sampled vector exceeds a prescribed threshold condition. Second, an asynchronous premise reconstruct method for T-S fuzzy systems is presented, which relaxes the assumption of the prior work that the premises of the plant and the filter are synchronous. Third, the resulting filtering error system is modeled under consideration of event-triggered communication, sensor failure, and asynchronous premise in a unified framework. By adopting the Lyapunov functional method and integral inequality approach, a delay-dependent criterion is developed to guarantee asymptotic stability for the filtering error systems and achieve strict (Q, S, R) - α dissipativity. Consequently, suitable filters and the event parameters can be derived by solving a set of linear matrix inequalities. Finally, an example is given to show the effectiveness of the proposed method.

This paper considers nonfragile exponential synchronization for complex dynamical networks (CDNs) with time-varying coupling delay. The sampled-data feedback control, which is assumed to allow norm-bounded uncertainty and involves a constant signal transmission delay, is constructed for the first time in this paper. By constructing a suitable augmented Lyapunov function, and with the help of introduced integral inequalities and employing the convex combination technique, a sufficient condition is developed, such that the nonfragile exponential stability of the error system is guaranteed. As a result, for the case of sampled-data control free of norm-bound uncertainties, some sufficient conditions of sampled-data synchronization criteria for the CDNs with time-varying coupling delay are presented. As the formulations are in the framework of linear matrix inequality, these conditions can be easily solved and implemented. Two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control.

In this paper, we investigate the problem of stability and stabilization for sampled-data fuzzy systems with state quantization. By using an input delay approach, the sampled-data fuzzy systems with state quantization are transformed into a continuous-time system with a delay in the state. The transformed system contains nondifferentiable time-varying state delay. Based on some integral techniques, some new stability and stabilization criteria are first proposed by a modified Lyapunov functional. Furthermore, in the case of no quantization, some new stability and stabilization criteria are also obtained. It is shown that the new stability and stabilization criteria can provide a larger upper bound of the sampling interval than some existing ones in the literature. Two simulation examples are given to show the effectiveness of the proposed design method.

In this brief, an extended dissipativity analysis was conducted for a neural network with time-varying delays. The concept of the extended dissipativity can be used to solve for the H∞, L2-L∞, passive, and dissipative performance by adjusting the weighting matrices in a new performance index. In addition, the activation function dividing method is modified by introducing a tuning parameter. Examples are provided to show the effectiveness and less conservatism of the proposed method.

This paper considers the problem of delay-dependent stability criteria for neural networks with time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative stability criterion is established in terms of linear matrix inequalities. Second, by proposing novel activation function conditions which have not been proposed so far, further improved stability criteria are proposed. Finally, three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea.