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 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 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.

The dynamic event-triggered (DET) bipartite consensus and attack estimation problem is investigated for multi-agent systems (MASs) under false data injection (FDI) attacks generated by exogenous systems. For the purpose of compensating for the system bias, a novel set of estimators are introduced to estimate the FDI attacks. To optimize the limited resources of the network bandwidth, a DET protocol dependant on the auxiliary variable and the local estimations is employed to regulate data transmission. By employing the designed state observer and attack observer, an novel attack tolerant event-triggered control strategy is established. Subsequently, sufficient criteria are obtained to ensure the bipartite consensus performance with$l_{2}$-$l_{\infty }$constraint. Then, by solving algebraic matrix equations and recursive linear matrix inequalities (LMI), the gains of state estimator, attack estimator and desired controller are determined, respectively. Finally, simulation examples are provided to demonstrate the validity of the derived results. Note to Practitioners—This paper discusses the secure bipartite consensus control problem for MASs subject to FDI attacks. Since the system state deviation caused by FDI attacks will bring about degradation of system performance, it is important to protect the MASs from being destroyed by designing observers to estimate the FDI attack and the system state. In view of the bandwidth constraints among the agents in MASs, optimization utilization of the network bandwidth should be taken into account seriously. Therefore, in this paper, the proposed observer-based DET controller is developed to guarantee bipartite consensus of the considered MASs while ensuring efficiency utilization of bandwidth resources. The current research provides a helpful reference for the secure bipartite consensus control of MASs with bandwidth constraints.

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.

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 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.

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 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.

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.