This paper investigates the distributed formation control problem of second-order multiagent systems (MASs) subject to unknown nonlinear dynamics. A predictor-based fuzzy learning framework is developed, in which fuzzy logic systems (FLSs) approximate the unknown nonlinear dynamics and a two-layer learning mechanism accelerates weight adaptation by utilizing the velocity prediction error. A distributed observer is designed for jointly connected switching directed topologies to handle limited leader accessibility, allowing agents to estimate the global reference trajectory using solely local information. Furthermore, an actor–critic reinforcement learning architecture is integrated to realize distributed optimal formation control, in which the value function is reformulated with a compensatory term that exploits available system structural information, thereby enhancing policy optimization efficiency and accelerating convergence. Simulation results demonstrate that the proposed approach exhibits improved robustness and accuracy in both dynamics approximation and position tracking when compared with conventional learning-based methods.

This article investigates the decentralized adaptive event-driven (AED) reliability control problem for nonlinear interconnected systems (NISs) with privacy-preserving. The decentralized optimal control strategy for the whole system is formulated by the optimal control for nominal subsystems, where an AED homomorphic cryptosystem is implemented for each subsystem to alleviate network burden while achieving security by encrypting transmitted signals. The Paillier cryptosystem with additive homomorphic properties is introduced to conceal the original data. Therefore, the transformed Hamilton–Jacobi–Bellman equations (HJBE) are constructed to facilitate cooperative optimization across subsystems within the framework of reinforcement learning. Subsequently, we leverage single critic networks to derive solutions to the HJBE, utilizing the experience replay approach for weight updates. Furthermore, by virtue of the Lyapunov function, the derived decentralized control law can force the whole NIS to be uniformly ultimately bounded stable. Eventually, numerical examples of NISs are provided to illustrate the effectiveness of the proposed optimization algorithm.

This study proposes a novel methodology driven by adaptive dynamic programming (ADP) to achieve optimal fault-tolerant tracking control (FTTC) in affine nonlinear systems (ANSs) subject to actuator failures. To begin with, a key innovation lies in constructing an extended system model that integrates tracking error dynamics and desired trajectory, establishing a basis for control synthesis. For this extended system, a critic neural network (NN) with adaptive weights is designed to approximate the discounted value function, enabling online optimization of control policies. Additionally, to address practical network constraints, a bandwidth-efficient event-triggered scheme (BEETS) is introduced to optimize limited bandwidth resources in networked control setups. Meanwhile, an NN based fault observer realizes real-time identification and dynamic compensation of actuator faults, mitigating their adverse effects on system performance. The resulting FTTC framework, which merges ADP-based optimal control with a fault compensator, ensures robust fault tolerance under both constant and time varying fault scenarios. Through rigorous Lyapunov stability assessment, uniform ultimate boundedness (UUB) is proven to be achieved for the closed-loop nonlinear system, guaranteeing stable tracking performance. The validity of this algorithm is verified through rigorous theoretical analysis and captivating numerical simulations.

This article proposes a synergistic model-based actor–critic (AC) learning framework for secure vehicle tracking under a dynamic event-triggered scheme (DETS) and subject to deception attacks. To explicitly resolve the conflict between communication conservation and learning convergence, the AC algorithm is structurally embedded within a discrete linear quadratic tracker (LQT) and backstepping architecture. This co-design utilizes the nominal linear structure as a rigorous prior to analytically derive update laws, ensuring stable Hamilton–Jacobi–Bellman approximation even when data are sparse and stochastically corrupted. By bounding the policy search space, it ensures a minimal number of learnable parameters and a clear physical interpretation. Lyapunov analysis rigorously proves the asymptotic convergence of the tracking errors to zero. Numerical simulations demonstrate superior tracking accuracy, faster convergence, and significant bandwidth savings compared to standard model-free reinforcement learning baselines.

This paper investigates the secure state estimation problem for time-varying singularly perturbed discrete sequential systems (SPDSSs) under bandwidth-constrained and multichannel-enabled wireless communication environment. Firstly, to avoid the potential data collision as well as fully utilize the available wireless channels, a round-robin-like multichannel access policy (RRL-MAP) is presented for scheduling the transmission of measurement signals. The considered SPDSS is further assumed to be compromised by deception attack, and then the measurement outputs are updated based on the RRL-MAP and the attack driven by a Bernoulli process. In light of the constructed measurement model, distributed recursive state estimators are designed with the proposing of an algorithm that determines the estimators’ gains. Finally, the efficiency of the developed state estimation method for the envisioned SPDSS is verified by both theoretical analysis and simulation experiments.Note to Practitioners—State estimation for SPDSSs is critical given the widespread application of the systems, however it is challenged by limited communication resource and stochastic cyber attacks. Traditional RR protocol is viewed as a desirable method for mitigating the limitation of network bandwidth, but which becomes inefficient under practical multichannel communication scenario. In addition, deception attack is acknowledged as a kind of hardly detected cyber attacks, and will seriously threaten the data integrity. In view of this, this paper presents the RRL-MAP to guarantee efficient data transmission by appropriately scheduling the available communication channels, and uses Bernoulli process to properly describe deception attack. Then, by further considering the structural characteristics of SPDSSs, distributed recursive state estimators are designed to achieve desired system performance. The effectiveness of the estimators is also comprehensively evaluated, which validates the practical applicability of the proposed state estimation method.

This article is centered on an optimal tracking control problem for nonlinear networked systems with limited network bandwidth and unavailable dynamics. Foremost, considering the absence of system dynamics, a state identifier grounded in generalized fuzzy hyperbolic model (GFHM) is estabilshed to eliminate the reliance on known system states. Meanwhile, in order to efficiently utilize the limited communication resources within the network, a dynamic event-triggered mechanism (DETM) with an adaptive threshold is adopted. Subsequently, the DETM-based Hamilton-Jacobi-Isaacs (HJI) equation is constructed following the Bellman optimality theory. Furthermore, we develop a particle swarm optimization (PSO)-driven reinforcement learning (RL) algorithm to achieve the desired optimal tracking control strategy. It is demonstrated that the tracking error system can be promised to be uniformly ultimately bounded (UUB) stability. Eventually, a numerical experiment is provided to illustrate the validity of the developed RL algorithm.Note to Practitioners—This study targets the optimal tracking control issue of unknown networked systems, which holds practical significance in various fields such as robotics, autonomous vehicles and industrial control. In real-world environments, the dynamics of networked systems are not always accessible, thus this paper employs GFHM to approximate unknown dynamic information. Additionally, due to the involvement of the network, the restriction of communication resources is an unavoidable concern. To address this, DETM is adopted to improve network transmission efficiency. Subsequently, to achieve the optimal tracking control, the HJI equation based on DETM is constructed. The solution to the HJI equation is obtained through the RL algorithm supported by critic neural network, with its weights trained using the PSO algorithm. Compared to the commonly applied gradient descent method, under the PSO algorithm, the initial weights can be randomly assigned, making implementation easier and increasing the success rate of system operation. This is of significant importance in practical applications. Besides, a simulation example illustrates the feasibility of the developed method.

This paper presents an investigation of the sliding-mode secure control for nonlinear semi-Markov jump systems in the presence of non-periodic denial-of-service attacks and false data injection attacks. First of all, we employ Takagi-Sugeno fuzzy model to describe the nonlinear semi-Markov jump control systems. In order to optimize transmission efficiency and control performance, a novel dynamic-memory event-triggered mechanism is developed by incorporating auxiliary dynamic variable and historical transmitted data. Then, a memory-based fuzzy sliding surface is put forward to attenuate the influences of stochastic cyber attacks with the aid of event-triggered state information. Moreover, by utilizing Lyapunov stability theory, sufficient conditions are derived to guarantee the exponentially mean-square stability of the system with an$H_{\infty}$performance index, even in the cases of generally uncertain and unknown transition rates. Furthermore, a memory-based sliding mode secure controller is designed to ensure the reachability of the predefined switching surface and desirable sliding motion within finite time. Finally, the efficacy of the proposed control scheme is demonstrated through a tunnel diode circuit model.Note to Practitioners—This study focuses on the issue of secure control for nonlinear semi-Markov jump systems, which holds practical significance across various domains, including applications in robotic manipulators, circuit models, and DC motors. We broaden the scope by considering more general jump parameter matrices to align more closely with the real-world system environment. Moreover, networked environments pose two primary challenges: network bandwidth constraints and the external network attacks. To tackle these issues, this paper introduces an innovative dynamic memory event triggering mechanism to enhance network transmission efficiency and optimize communication resource utilization. Meanwhile, to address cyber attacks resulting from the inherent openness of networks, the current study adopts a defensive sliding mode control strategy to provide robust protection against network attacks and disturbances. The effectiveness of the suggested approach is confirmed through a circuit system model. It is worth mentioning that the proposed method has the potential for broader application within real-world engineering scenarios, particularly those involving network-based nonlinear systems with various practical constraints.

This paper focuses on the problem of outlier-resistant distributed filtering under dynamic event-triggered scheme (DETS) and cyber-attacks over sensor networks. A novel distributed DETS is proposed, which allows us to minimize the data-releasing rate while simultaneously preventing abrupt data from being sent over the network. Consider the situation of measurement outliers in presence, a saturation function is used in distributed filter design to confine the contaminated signals so as to improve filtering performance. The occurrence of stochastic DoS attacks which may corrupt the measurement in the sensor networks are also considered. Sufficient conditions for the expected filter are derived while guaranteeing the asymptotically stability of the filtering error system with a guaranteed$H_{\infty}$performance index$\gamma$. A numerical simulation is given to illustrate the effectiveness of the theoretical analysis and design method.Note to Practitioners—The issue of filtering over sensor networks is subject to network impections induced by the limited communication resources and cyber-attacks. Since these imperfections may lead to performance degration or instability of the system, it is critical to take some measures to reduce unnecessary signal communication among sensor nodes and eliminate the adverse effects of cyber-attacks. Besides, the measurement outliers are frequently encountered due to sensor aging or abnormal disturbances, which may impair the accuracy of the estimation resuts. In this paper, the outlier-resistant distributed filtering problem is investigated in sensor networks subject to dynamic event-triggered schemes and DoS attacks. A co-design method of the DETS and the distributed filter is propsed which can gurantee the desired system performance.

This article investigates the output feedback stabilization problem for a parabolic partial differential equation (PDE) system with uncertain parameters, which is subject to limited communication resources. First, to alleviate the burden of channel communication and avert data collision, the multichannel measurement outputs are scheduled by the weighted try-once-discard protocol. Moreover, to bridge the gap between theory and reality, the Markov process is utilized to depict the occurrence of various actuator faults in a stochastic manner. An impulsive closed-loop model is established in light of the network-induced delay and various stochastically occurring actuator faults. Following the model, sufficient criteria for guaranteeing the input-to-state stability of the PDE system are put forward by constructing a new Lyapunov functional. Then, one can deduce the determination of the controller gains and weighted matrices by employing the technique of linearizing the matrix inequalities. Finally, simulation results unequivocally demonstrate the effectiveness of the proposed method.

From the perspective of industrial production reliability, a robust event-triggered (ET) control strategy is presented for uncertain continuous stirred tank reactor (CSTR) system with asymmetric input constraints. To begin with, we propose a nonquadratic performance function to transform the robust control issue by constructing the relevant auxiliary dynamics. For effectively mitigating the pressure of data transmission and controller execution, a dynamic ET scheme (DETS) with an adjustable threshold function is adopted. Subsequently, we formulate the DETS-based Hamilton–Jacobi–Bellman (DET-HJB) equation according to optimality theory. In addition, a DETS-assisted reinforcement learning algorithm with a unique critic neural network can efficiently tackle the derived DET-HJB equation. Meanwhile, the corresponding critic weight is regulated on the basis of gradient descent technique and experience replay approach. By presenting a rigorous analysis under two situations, the uniform ultimate boundedness of auxiliary dynamics and weight approximation error can be ensured. Eventually, the feasibility of the proposed algorithm is demonstrated by experimental results of CSTR system.

The article designs a secure co-estimation approach for nonlinear systems with false data injection (FDI) attack and network bandwidth limitations, which is described by interval type-2 (IT2) fuzzy system. For mitigating communication load and avoiding network data collision, an event-triggered weighted try-once-discard protocol is implemented to adjust the data transmission frequency. Given the unknown system state and FDI attacks, both of the state observer and the attack observer are put forward. Besides, a new attack compensation controller is proposed under co-design of the observer of the state and FDI attack. The asymptotical stability of the augmented system can be realized by the proposed sufficient conditions for the existence of the fuzzy observer and controller gains, while the prescribedH∞performance requirement can be met. Then the secure controller design method is given accordingly. Finally, the validity of the attained results are provided by simulation examples.

This article explores the distributed set-membership state estimation problem over sensor networks (SNs) with chaotic encrypted privacy-preserving scheme and multistrategy injection attacks (MIAs). Since potential eavesdroppers in communication networks may intercept the transmitted measurement signals, chaotic encryption is adopted as a privacy-preserving scheme to protect the system state information from being revealed. The measurement signals are encrypted before transmission and decrypted upon reception by the remote estimator. A newly devised attack model is developed to characterize the injection attacks, which occur randomly and involve a combination of multiple attack strategies. By employing matrix inequality techniques, a unified set-membership estimation scheme is developed when both the privacy-preserving scheme and the MIAs coexist. Subsequently, based on the sufficient condition of constraining the estimation error within an ellipsoidal range, an optimization problem is formulated to achieve the optimal estimation performance at each time step, along with the development of a recursive algorithm for computing the required estimator parameters. Finally, simulation is provided to verify the set-membership estimation approach under the chaotic encryption scheme.

This paper investigates the optimal decentralized control issue for a class of networked interconnected systems (NISs) under communication and control constraints. First of all, the decentralized optimal control problem for NISs is transformed into the optimal control issue for nominal systems. And then the event-triggered Hamilton-Jacobi-Isaac (HJI) equations are derived with optimal control theory based on the constructed cost function and adopted adaptive event-triggered mechanism (AETM). Furthermore, a Reinforcement Learning (RL) based event-triggered optimal control algorithm with actor-critic networks is proposed to obtain numerical solutions of the HJI equations and the Uniformly Ultimately Bounded (UUB) stability of NISs is proved to be guaranteed with Lyapunov stability theorem. Eventually, a simulation experiment is conducted to verify the effectiveness of proposed RL-based optimal decentralized algorithm with AETM.Note to Practitioners—Actually, the limitation of communication resource is one of the key factors leading to performance degradation of NISs. It is crucial to cope with this issue in the control of NISs. Employing proper communication protocol is an effective way to diminish the adverse effects of undesirable phenomena, such as data loss and out-of-order. In addition, given the scale is gradually expanded and the structure is gradually complex for NISs, the operation cost is a problem that cannot be ignored in the control process of NISs. Thus, it is significant and challenging to minimize the operation cost on the basis of ensuring system stability. In view of the communication constraints and optimization problem, the HJI equations based on AETM are derived to model the decentralized optimal control problem for NISs. Moreover, a RL-based iterative algorithm is proposed to derive the numerical solutions to HJI equations and further effectively stabilize the targeted control constrained NISs in the sense of UUB.

This article addresses the fuzzy filtering problem for nonlinear systems based on the interval type-2 fuzzy model. To make better use of the constrained resources for communication, a novel event-triggering weighted try-once-discard protocol (ET-WTODP) is proposed. Different from the existing diverse protocols, ET-WTODP only allows a portion of components with relatively greater deviation to transmit at each triggering moment. The fuzzy filter is constructed with imperfectly matched membership functions and mode-dependent parameters. Denial-of-service (DoS) attacks frequently occur in communication networks are also considered. The overall fuzzy filtering error system (OFES) model is established taking into account ET-WTODP and DoS attacks. The sufficient conditions for the OFES to be asymptotically stable with a preset$H_{\infty }$performance level are identified by using the Lyapunov stability theory to obtain the desired asynchronous filter. Finally, numerical simulations are presented for verification that the proposed filtering approach is feasible and valid.

This article is concerned with the secure event-triggered (ET) control issue for interval type-2 (IT-2) fuzzy networked systems under stochastic communication (SC) protocol and random false data injection attacks. On account of the realistic physical sensor constraint, a saturation function is utilized in the measured output. In an effort to mitigate the network burden and reduce the incidence of data collision, a novel dynamic ET SC protocol is proposed by integrating with the major merits of the dynamic ET strategy and SC protocol. Moreover, the fuzzy observer-based controller in the presence of different membership functions is constructed. Subsequently, some sufficient design conditions are put forward for satisfying the asymptotically stable performance of the augmented closed-loop IT-2 fuzzy model. In addition, the gain parameters of fuzzy observer and controller can be obtained by virtue of solvable sufficient criteria without nonlinear terms. Eventually, the effectiveness of the theoretical secure fuzzy control scheme is verified by a numerical example.

In this article, a decentralized event-triggered (ET) regulation problem is tackled for networked interconnected systems (NISs) with control constraints and unmatched interference. Foremost, the decentralized regulation issue is converted into the optimal control problems for the associated auxiliary subsystem. In confronting the unavailability of system dynamics, the utilization of generalized fuzzy hyperbolic models-assisted identifier provides a novel perspective to devise the efficacious control policy for the constrained NISs. For the sake of mitigating the communication workload, a new dual threshold functions-based adaptive ET scheme (DTAETS) is put forward by incorporating the current data and latest ET signal. Moreover, we present a data-driven decentralized reinforcement learning algorithm to acquire the solution of DTAETS-boosted Hamilton–Jacobi–Isaacs equation. Then, the uniformly ultimately bounded stability of auxiliary subsystem and the weight estimation error is assured. Ultimately, a numeral experiment is conducted to substantiate the validity of the theoretical results.

This paper is concerned with the reinforcement learning-based tracking control problem for a class of networked systems subject to denial-of-service (DoS) attacks. Taking the effects of DoS attacks into consideration, a novel value function is proposed, which considers the cost of the control input, external disturbance and tracking error. Then, using the structure of the value function, the tracking Bellman equation and Hamilton function are defined. By employing the Bellman optimality theory, the optimal control strategy and the game algebraic Riccati equation (GARE) are solved with the Hamilton function. Next, the desired tracking performance is guaranteed as the solution of the GARE is found. Furthermore, an attacks-based Q-learning algorithm is projected to find the solution to the optimal tracking problem without the system dynamics and the convergence of the Q-learning algorithm is given. Finally, the F-404 aircraft engine system is given to verify the effectiveness of the proposed control strategy.

In this article, the outlier-resistant (OR) observer-based secure control problem is investigated for nonlinear networked systems under randomly activated denial-of-service (RADoS) attacks and stochastic communication (SC) protocol. In virtue of zero-order holder (ZOH) technique, an attack compensation scheme is adopted to alleviate the adverse effects brought by RADoS attacks. By introducing multiple internal dynamic variables (MIDVs), a novel multi-variable event-triggered stochastic communication (MVET-SC) protocol is put forward to enhance the design flexibility and prevent the occurrence of data conflicts. Moreover, the non-fragile fuzzy controller is applied due to the inaccuracies in practical applications. Comprehensively taking the above factors into account, the asymptotic stability of the augmented fuzzy system can be ensured under the presented sufficient criteria. Meanwhile, the parameters of the OR observer-based secure controller are derived according to the obtained design conditions. In the end, a simulation example of mass-spring-damping system (MSDS) demonstrates the validity of the non-fragile secure control approach with OR observer.

In this article, the cooperative output regulation problem (CORP) for unknown linear multiagent systems (MASs) is studied on the basis of a data-driven approach. Different from existing literature, first, the proposed method does not require prior knowledge of the system matrices and the leader system where the control gains are designed using input and state data. Next, the solution to the unknown regulator equations is derived using the data informativity approach. Then, to speed up the convergence rate, a distributed deadbeat control law is proposed for the MASs. For the purpose, the leader's system matrix is identified according to the measured data, and then a distributed observer is employed to estimate the state of the leader system in a distributed manner. Furthermore, the CORP is converted to a stabilization problem by using the data-based solution of the regulator equations. On the basis of the distributed observer, a data-driven deadbeat control law is designed in the context where the system matrices of the plant are completely unknown. It is guaranteed that the tracking error for each agent can converge to the origin asymptotically. Finally, an example is conducted to illustrate the effectiveness of the proposed data-based framework.

Synchronization control is an important issue in complex networks (CNs), but the specific realization is generally influenced by many factors. In this article, by taking into accounts of the coupling effect and cyber attacks, we dedicate to design an efficient synchronization control approach for discrete-time CNs. Given that the coupling among nodes in CNs inevitably introduces the internode data exchange, round robin (RR) protocol is adopted to prevent data collision caused by the limitation of communication resources. Moreover, a probabilistic interval model is employed to capture the characteristics of the coupling delay, i.e., the delay of the internode data transmission. The deception attacks launched on the multichannel-enabled communication network between controllers and actuators of nodes in CNs are also considered. Then, a synchronization error model is established to describe the concerned synchronization control problem. By defining appropriate Lyapunov–Krsasovskii function, the sufficient conditions for the stability of the envisioned synchronization error system are obtained, and the design method for controllers is proposed subsequently. Simulations are finally conducted to demonstrate the validity of the work.

This article addresses a security control issue for discrete-time networked control systems (DTNCSs) with uncertainty under stochastic communication protocol (SCP) characterizing a class of networked systems, such as autonomous dynamics. Specially, in order to properly take account of the constructed system in practical application, the time-varying uncertainty with norm-bounded parameter is adopted in DTNCSs. To avoid data conflict in the communication network with limited bandwidth, SCP modeled by discrete-time Markov chain is introduced to schedule the transmitted signals, which are subjected to deception attacks described by a random process. In addition, the mode-dependent observer and controller are utilized in line with the scheduling situations led by SCP. Moreover, the stochastic stability of the augmented dynamics is ensured by the presented sufficient condition. Meanwhile, the corresponding gain parameters of the observer and controller can be computed. Ultimately, two simulation experiments are utilized to validate the availability of the proposed security control strategy.

This article investigates the FlexRay (FR) protocol-based dynamic event-triggered (DET) secure filtering problem with the high-rate communication (HRC) network for the interval type-2 (IT-2) fuzzy networked system. For fear of data collisions and preempting limited communication resources, a novel DET-FR (DETFR) protocol is adopted to schedule sensor nodes. Different sensors are employed for distinct protocols and data-holding strategies to reinforce the efficiency of network transmission. Moreover, considering the random occurrence of cyber attacks and fading measurements in the HRC channel, the attack parameters are represented by the Bernoulli process, and measurement attenuation coefficients are expressed by the random variables of equidistribution on a fixed interval. Based on this, to ensure that the error dynamics meet the disturbance attenuation level, several sufficient conditions are given. Subsequently, the DETFR protocol-based secure filtering algorithm is developed to acquire the fuzzy filter parameters. Eventually, the validity of our designed secure filtering algorithm is proved through two examples.

This article explores the state estimation problem of memristive neural networks (MNNs) with mixed delays subject to multichannel round-robin protocol (MRRP) and multimodal injection attacks (MIAs). MRRP allows for multichannel-enabled signal transmission between sensors and remote estimators, thereby significantly mitigating network congestion. Different types of attacks are supposed to be encountered during the sensor data transmission via network. Both the impacts of the MRRP and the attacks are reflected in modeling the addressed system. The purpose of this article is to design an estimator capable of constraining the estimation error within an ellipsoidal region in the presence of noise interference, mixed delays, MRRP, and MIAs. Using recursive matrix inequality (RMI) techniques, sufficient conditions are derived for achieving the desired performance and the expected state estimator gains are derived by minimizing the constrained ellipsoid region. Ultimately, the efficacy of the developed estimate strategy is demonstrated through numerical simulation.

This article is concerned with the observer-based security fuzzy control problem for a class of nonlinear networked systems with imperfectly matched membership functions (MFs) under weighted try-once-discard (WTOD) protocol. The nonlinear networked system is constructed based on interval type-2 Takagi–Sugeno model, which can properly handle parameter uncertainties via lower and upper MFs. To mitigate the communication load, WTOD protocol is exploited to schedule the transmission order of the signals, in which those signals may be subjected to randomly occurring deception attacks. The observer is constructed under immeasurable premise variables, and then the controller with imperfect matching MFs is designed according to the estimated states. Moreover, the observer-based controller is derived with Lyapunov stability theory such that the stochastic stability of the augmented closed-loop system with predefined disturbance attenuation performance can be guaranteed. Subsequently, sufficient conditions are provided for the desired observer and controller gain matrices based on linear matrix inequalities technique. Numerical simulation example demonstrates the validity of the proposed observer-based security fuzzy control design scheme.

This article is focused on the adaptive event-based interval type-2 (IT-2) fuzzy security control problem for networked control systems with multiple network attacks. In order to save the network resources, this article proposes an improved adaptive event-triggered model, which can adjust the threshold dynamically according to the change of current signal and previous triggering signal. In addition, due to the change of external environment, the impact of randomly occurring output bias is considered in the systems. Taking the effects of multiple network attacks and adaptive event-triggered scheme into consideration, a class of augmented error fuzzy system model is established. Next, some sufficient conditions are obtained for the stochastic stability with an$H_{\infty }$performance index via the Lyapunov stability analysis method. The corresponding parameters of the IT-2 fuzzy controllers and observers are also derived by some linear matrix inequalities. Finally, two simulation examples are given to support the advantages of the proposed method.

This article concentrates on optimal tracking control for a class of nonlinear networked systems subjecting to limited network bandwidth and unmatched disturbance. Given the models of the control and reference systems, the considered optimal tracking control issue is initially formulated as a minimax optimization problem. Then, with the introduction of an event-triggered mechanism used for saving bandwidth, the formulated problem is transformed into solving an event-based Hamilton-Jacobi-Isaacs (HJI) equation by recurring to the Bellman optimality theory. Based on the HJI equation, we demonstrate that the stability of the concerned system in the sense of uniformly ultimately bounded (UUB) can be guaranteed with the envisioned optimal control and worst disturbance policies. Here, the disturbance policy can be varied periodically while the control policy can only be updated at event-triggering instants, which differs from the existed researches. Furthermore, we propose a reinforcement learning (RL)-based algorithm to handle the constructed HJI equation and thus settle the studied tracking control problem. The effectiveness of the algorithm is finally validated by both theoretical analysis and simulations.

This article investigates the issue of observer-based security control for the interconnected semi-Markovian jump systems with completely unknown and uncertain bounded transition probabilities (TPs). Considering the limited bandwidth of communication network in each subsystem, an adaptive event-triggered mechanism (AETM) is developed to relieve more network burden than the conventional event-triggered mechanism (ETM), where the designed adaptive law can dynamically adjust the triggering threshold. In addition, two Bernoulli distributed variables are utilized to describe the influence of denial-of-service (DoS) attacks and false-data injection (FDI) attacks in the proposed observer-based security control strategy. Moreover, some sufficient criterions are derived for the stochastic stability with an$H_{\infty }$attenuation level of augmented systems. Meanwhile, the observer and controller gain matrices can be attained simultaneously with the help of linear matrix inequalities (LMIs). Finally, we provide a practical example to demonstrate the effectiveness of theoretical results.

This article is concerned with the problem of the$H_{\infty }$output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed$H_{\infty }$performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

The problem of enhanced stabilization of discrete-time Takagi–Sugeno fuzzy system is investigated in depth via proposing a new way to employ much more information implied in various time-varying differences of multi-instant normalized fuzzy weighting functions. Benefit from the given comprehensive real-time scheduling model, these time-varying differences on time vertical dimension and two different transverse dimensions are for the first time integrated into fuzzy stabilization and two key alterable weights are also introduced. Consequently, much considerable freedom can be produced and, thus, it brings much less conservative results than the reported ones in recent literature. More importantly, a very simple workable way has been provided to online recognize the specific enabled mode at each sampling instant so that those existing time-consuming online operations in referred literature can be avoided to ease online implementation costs. Indeed, this feature is very beneficial to the practical application of our theoretical results. Finally, the superiority of our developed method over previous ones is validated adequately via two benchmark simulations.

The problem of secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack is investigated in this article. First, a new model of hybrid cyber attack, which considers a deception attack, a replay attack, and a denial-of-service (DoS) attack, is established for filter design. Second, an adaptive event-triggered scheme is applied to the filter design to save the limited communication resource. In addition, a novel adaptive-event-triggered filtering error model is established with the consideration of hybrid cyber attack and input constraint. Moreover, based on the Lyapunov stability theory and linear matrix inequality technique, sufficient conditions are obtained to guarantee the augmented system stability, and the parameters of the designed filter are presented with explicit forms. Finally, the proposed method is validated by simulation examples.

This article concentrates on event-based secure leader-following consensus control for multiagent systems (MASs) with multiple cyber attacks, which contain replay attacks and denial-of-service (DoS) attacks. A new multiple cyber-attacks model is first built by considering replay attacks and DoS attacks simultaneously. Different from the existing researches on MASs with a fixed topological graph, the changes of communication topologies caused by DoS attacks are considered for MASs. Besides, an event-triggered mechanism is adopted for mitigating a load of network bandwidth by scheduling the transmission of sampled data. Then, an event-based consensus control protocol is first developed for MASs subjected to multiple cyber attacks. In view of this, by using the Lyapunov stability theory, sufficient conditions are obtained to ensure the mean-square exponential consensus of MASs. Furthermore, the event-based controller gain is derived by solving a set of linear matrix inequalities. Finally, an example is simulated for confirming the effectiveness of the theoretical results.

This paper investigates the controller design problem of networked control systems subject to cyber attacks. A hybrid-triggering communication strategy is employed to save the limited communication resources. State measurements are transmitted over a communication network and may be corrupted by cyber attacks. The aim of this paper is to design a controller for a new closed-loop system model with consideration of randomly occurring cyber attacks and the hybrid-triggering scheme. A stability criterion is obtained for the system stabilization by employing Lyapunov stability theory and stochastic analysis techniques. Moreover, the desired controller gain is derived by resorting to some matrix inequalities. Finally, a numerical example is exploited to demonstrate the usefulness of the proposed scheme.

This article focuses on the security control for Takagi-Sugeno (T-S) fuzzy systems with adaptive event-triggered mechanism (AETM) and multiple cyber-attacks, which include deception attacks and denial-of-service (DoS) attacks. A multiple cyber-attacks model is first established for T-S fuzzy systems by considering deception attacks and DoS attacks at the same time. An AETM is introduced to relieve the network load, where the threshold of event-triggering condition can be adaptively adjusted while preserving the system performance. Then a novel mathematical model for T-S fuzzy systems with multiple cyber-attacks and AETM is proposed first. Based on the built model, sufficient conditions to guarantee the exponentially mean square stability of the system are achieved by utilizing the Lyapunov stability theory. Moreover, the controller gains are derived with the help of a linear matrix inequality technique. Finally, simulated examples are presented for illustrating the effectiveness of the proposed method.

This paper focuses on the hybrid-driven-based H∞control for networked cascade control systems (NCCSs) with actuator saturations and stochastic cyber attacks. In order to relieve the network bandwidth load effectively, a hybrid triggered scheme is introduced, which contains a switch between time-triggered scheme and event-triggered scheme. A newly hybrid-driven-based NCCS model is established by considering the effects of both actuator saturations and stochastic cyber attack, which is an important threat to network security. By using the Lyapunov stability theory, sufficient conditions are obtained to ensure the system stability. Furthermore, both primary controller gain and secondary controller gain are achieved explicitly in terms of linear matrix inequality techniques. Finally, a power plant gas-turbine system is presented to illustrate the usefulness of the designed state feedback controllers.

This paper investigates the problem of event-triggered H∞load frequency control (LFC) for multiarea power systems under hybrid cyber attacks, including denial-of-service (DoS) attacks and deception attacks. An event-triggered transmission scheme is developed under the DoS attacks to lighten the load of network bandwidth while preserving a satisfactory system performance. Then, a new switched system model accounting for the simultaneous presence of DoS attacks and stochastic deception attacks is established with respect to the LFC for multiarea power system. On the basis of the new model, sufficient conditions ensuring multiarea power system exponentially mean-square stable with prescribed H∞performance are obtained by using Lyapunov stability theory. Furthermore, criteria for simultaneously designing the weighting matrix in event-triggered scheme and the controller gain matrix are derived by utilizing the linear matrix inequality technique. Finally, a three-area power system is simulated to demonstrate the usefulness of the approaches proposed in this paper.

This paper examines quantized stabilization for Takagi-Sugeno (T-S) fuzzy systems with a hybrid-triggered mechanism and stochastic cyber-attacks. A hybrid-triggered scheme, which is described by a Bernoulli variable, is adopted to mitigate the burden of the network. By taking the effect of the hybrid-triggered scheme and stochastic cyber-attacks into consideration, a mathematical model for a closed-loop control system with quantization is constructed. Theorems for main results are developed to guarantee the asymptotical stability of networked control systems by using Lyapunov stability theory and linear matrix inequality techniques. Based on the derived sufficient conditions in theorems, the controller gains are presented in an explicit form. Finally, two practical examples demonstrate the feasibility of designed algorithm.

This study is concerned with the reliable filtering problem for the sampled‐data system subject to a class of probabilistic sensor signals distortion. A new distortion model is developed by introducing a diagonal random matrix whose elements obey the Gaussian distribution. The main purpose in this study is to design a filter such that the error dynamics of the filtering process subject to the probabilistic sensor signal distortion is mean‐square asymptotically stable. Based on the modified delay‐central‐point (DCP) method and the convexity property of the matrix inequality, new criteria are derived for the existence of the desired H ∞ filters, by which it leads to much less conservative analysis results. Simulation results are provided to illustrate the effectiveness of the proposed method.