Information disclosure and cyber attacks pose significant challenges to practical implementation of distributed control in microgrids (MGs). This paper investigates the distributed privacy-preserving resilient secondary control problem for an isolated AC MG under hybrid attacks including false data injection (FDI) and denial-of-service (DoS) attacks at low communication costs. Specifically, using state decomposition strategy and event-triggered mechanism, a distributed privacy-preserving event-triggered resilient secondary controller is designed, which reduces communication while safeguarding system privacy. Additionally, a long short-term memory (LSTM)-based estimator is designed to estimate the aggregated communication signals from neighboring DGs. The proposed resilient control method can further mitigate the impact of cyber attacks by using the estimated aggregated communication signals as the reference value, and the stability of the MG system is proven by Lyapunov theory. Compared to existing data-driven methods for attack detection, the proposed LSTM-based estimator uses communication signals as the database, thus avoiding the leakage of complete system data during offline training. To validate the effectiveness of the proposed method, a hardware-in-the-loop experiment is conducted under various hybrid attacks in OPAL-RT.

Vehicle platoon control is one of the promising technologies to improve the performance of transportation systems. However, communication connections among vehicles in cyber physical vehicle systems (CPVS) are often intermittent due to environmental factors and limitations of physical equipment. Moreover, the system models of the vehicles are generally unknown in practice, making it challenging to implement platoon control for CPVS. To address these challenges, we investigate the challenge platoon control problem for CPVS with intermittent communication, where the system models of both the leader vehicle and the follower vehicles are unknown. A data-driven-based learning algorithm is first designed to obtain the unknown leader model. Then, based on the learned leader model, a finite-time distributed observer is proposed to estimate the leader vehicle state in finite time under intermittent communication. Furthermore, with the unknown system models, a data-driven-based controller gain learning algorithm is proposed to learn the controller gain. Based on the learned controller gain, adaptive decentralized tracking controllers are designed to perform platoon control for CPVS. Finally, the effectiveness of our result is examined by a simulation example.

In this article, the fixed-time resilient distributed Nash equilibrium (NE) control problem is addressed for nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks. Unlike existing results on NE seeking in noncooperative games, this article first investigates the layered fixed-time resilient distributed NE control problem for nonlinear MASs against DoS attacks, independent of initial conditions. To address these challenges, the novel layered NE control strategy is proposed, comprising a resilient fixed-time distributed NE seeking algorithm layer, an improved fixed-time performance enhancement layer, and an adaptive controller design layer. Specifically, a fixed-time resilient distributed NE seeking algorithm is first designed to guarantee players actions to converge toward the NE against DoS attacks. Then, novel high-order fixed-time filters are proposed to generate improved actions with smooth characteristics and converge to the actions in the aforementioned fixed-time algorithm. Based on the developed filters, a decentralized fuzzy adaptive controller is developed to achieve bounded tracking within the fixed time using the backstepping technique. Finally, a numerical simulation is conducted to validate the efficacy of the developed method.

This article investigates the resilient distributed Nash equilibrium (NE) control problem for nonlinear multiagent systems (MASs) that suffers from denial-of-service (DoS) attacks in the communication network. Different from the existing works on NE seeking in noncooperative games, it is the first trial to consider the resilient distributed NE control problem for nonlinear MASs under DoS attacks. To overcome the challenges caused by the considered problem, a new layered NE control method is developed, which consists of a resilient distributed NE seeking algorithm, two-stage cascade filters, and a resilient adaptive controller. Specifically, the resilient distributed NE seeking algorithm is proposed to ensure that the actions in this algorithm converge to the NE even under DoS attacks. Then, the improved actions with smooth characteristics are designed by introducing novel two-stage cascade filters. By using newly designed actions and their derivatives, a resilient adaptive controller is proposed to ensure that the output of MASs converges to the NE. Finally, simulation results are provided to verify the effectiveness of the proposed strategy.

In this article, the cooperative fuzzy tracking control problem for a type of high-order nonlinear MASs under the Round-Robin (RR) communication protocol is solved. To effectively reduce the communication among agents, an RR communication protocol is first proposed, where each agent can communicate with only one neighbor at any given time. Based on the developed communication protocol, a hierarchical control approach is introduced, consisting of a distributed observer layer that utilizes information from both local and neighboring agents, and a decentralized control layer that operates solely based on local agent information. Specifically, a distributed observer is designed in the distributed observer layer to achieve the distributed observation objective by introducing a time-varying gain in the observer input. Furthermore, an improved distributed observer with the upper triangular form is constructed to produce a signal with high-order derivatives. In the decentralized control layer, fuzzy adaptive controllers are designed for all agents by using the backstepping technique. By using the Lyapunov stability theory, rigorous stability analysis shows that the cooperative fuzzy tracking control problem is solved under the developed hierarchical control approach. In the end, a simulation example is presented to demonstrate the validity of our proposed method.

In this article, a distributed secure state estimation methodology is presented for Lipschitz nonlinear cyber-physical systems under deception attacks. By using the locally observable decomposition and the estimate communication with neighbors, secure sate estimation can be achieved under joint observability. Different from the existing results, an adaptive mechanism is developed so that the deception attacks can be compensated on the observer estimation performance. Moreover, the adaptive mechanism is designed in an independent structure to the main state observer which offers more freedom for both attack-free and attacked situations. Finally, simulation results are provided to verify the effectiveness of the proposed method.

This paper addresses the problem of vehicle platoon control for third-order nonlinear cyber physical vehicle systems (CPVSs) within finite-time under denial-of-service (DoS) attacks. Unlike existing approaches that assume known systematic matrix of the leader vehicle, this study proposes a data-driven learning algorithm to learn unknown systematic matrix of the leader vehicle. Additionally, a finite-time distributed observer is introduced, thereby enabling follower vehicles to achieve finite-time state observation of the leader vehicle under DoS attacks. Moreover, a novel low-pass filter chain is designed to construct a new variable with high-order derivatives. Utilizing the new variable, a finite-time resilient decentralized controller is formulated, incorporating fuzzy adaptive methods and backstepping techniques to achieve finite-time vehicle platoon control under DoS attacks. Finally, simulation experiments validate the effectiveness of the proposed method.

In this paper, we investigate the distributed resilient control problem of voltage and frequency restoration in AC microgrids (MGs) subject to false data injection (FDI) attacks under event-triggered communication. To solve the problem, new distributed dynamic event-triggered$k$-step attack observers are primarily designed for the accurate estimation of attacks while avoiding continuous communication. Based on the observer state, cooperative resilient controllers for voltage and frequency restoration are proposed, and the convergence of the voltage and frequency of the AC MGs as well as the exclusion of Zeno behavior are proved theoretically. Different from the existing event-triggered resilient control methods in AC MGs subject to unknown bounded FDI attacks, the developed distributed dynamic event-triggered based$k$-step attack observers and cooperative resilient controllers can guarantee accurate observation and compensation for FDI attacks. Finally, experimental tests are carried out to validate the effectiveness of the proposed control method, which demonstrates great resilience in maintaining stable operations under FDI attacks and represents a dramatic reduction in communication costs.

In this paper, the distributed secondary voltage and frequency restoration problem is addressed in AC microgrid systems subjected to hybrid false data injection (FDI) and denial-of-service (DoS) attacks. Firstly, a new distributed resilient iterative estimator based on the k-step estimation method is proposed that can accurately estimate the FDI attack signals as well as the voltage and frequency of each distributed generation (DG) even under the impact of DoS attacks. Then, based on the mean value of the attack estimation signal, a distributed resilient secondary controller is designed to compensate for the considered hybrid attacks. Compared with existing researches on resilient control of AC microgrids under hybrid attacks, the voltage and frequency regulation errors of the microgrid system converge to zero for the first time. Finally, the precise convergence of voltage and frequency in the AC microgrid system under the proposed method is verified through a real-time controller-hardware-in-the-loop experiment in OPAL-RT.

In this paper, the distributed fuzzy tracking control problem is investigated for high-order fuzzy nonlinear multiagent systems (MASs) under denial-of-service attacks. The proposed method is distinct from existing approaches in that it can accommodate scenarios where the model parameters of the reference systems are unknown, and the MAS is high order and nonlinear. Specifically, a data-driven algorithm is first introduced to learn the unknown reference system matrix. Based on the learned matrix, a distributed resilient observer and an improved observer are, respectively, designed to guarantee that both observers can observe the reference system state and that the high-order derivative of the improved observer state exists. By using the improved observer state and its high-order derivatives, a decentralized adaptive fuzzy controller is designed for each agent based on the backstepping technique. It is shown that the distributed resilient tracking can be achieved by the proposed method. Finally, a simulation example is proposed to show the effectiveness of the developed method.

In this paper, we study the cooperative optimization problem (COP) for fuzzy nonlinear multi-agent systems (MASs) subjecting to denial-of-service (DoS) attacks. Unlike the existing cooperative optimization results, both fuzzy nonlinear systems and DoS attacks are considered in this paper. To solve the problem, a hierarchical equivalence mechanism is first introduced to transform the COP into an equivalent one consisting of both a distributed optimization problem (DOP) and a decentralized tracking problem (DTP). By introducing an optimal distributed algorithm, a customized virtual signal is devised to minimize the optimization function and simultaneously mitigate the effects of DoS attacks. To construct a bridge between solving the COP and the DTP, a local reference generator, which includes the characteristics of existing high-order derivatives, is designed utilizing the Hermite interpolation method. Subsequently, the backstepping technique is employed to create a decentralized fuzzy adaptive controller. It is illustrated that the proposed method is capable of achieving the cooperative optimization objective. Finally, the theoretical results are successfully applied to show the efficiency of the proposed method.

In this paper, the prescribed-time cooperative resilient fuzzy tracking control problem is addressed for thirdorder nonlinear cyber-physical vehicle systems (CPVS) under denial-of-service (DoS) attacks. Different from the existing results on cooperative resilient fuzzy tracking control, the developed method can achieve the cooperative resilient fuzzy tracking control objective within the prescribed time. To begin with, a data-driven-based online learning algorithm is introduced for learning the unknown and switching matrix of the reference signal. Based on the learned matrix, novel distributed resilient cooperative observers are designed to achieve prescribed-time observation by introducing an asynchronous observing method. To further improve the smoothness of the observation signal, a new improved smooth second-order prescribed-time observer is designed. Furthermore, leveraging the states of the smooth observer, a fuzzy controller is proposed to achieve precise tracking within the prescribed time, independent of initial conditions using the scaling error method and the backstepping technique. Finally, a simulation example is included to illustrate the effectiveness of the proposed methodology

This article studies the cooperative resilient tracking control problem for nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks and time delays. In comparison to the existing distributed control results based on the output observability condition, a joint output observability condition, which is a more universal one, is considered in this article. Based on this, a novel cooperative resilient observer is designed to estimate the state of the reference system subjected to DoS attacks and time delays in the communication topology. Then, a new observed variable with high-order derivatives is constructed by designing a novel chain of low-pass filters. Furthermore, with the help of the newly constructed variables, a fuzzy controller is proposed by using the adaptive method and backstepping technology to ensure the semiglobally uniformly ultimately boundedness for tracking errors. Finally, a simulation example is included to showcase the effectiveness of the proposed method.

This article studies the distributed quantized tracking control problem with prescribed output performance requirements for uncertain nonlinear integer high-order multiagent systems (MASs) with both Denial-of-Service (DoS) attacks and external unknown disturbances under data-rate-constrained communication networks. A hierarchical control framework including a quantized resilient distributed observer layer and a decentralized quantized adaptive fractional-order controller layer is proposed for solving the considered control problem. Specifically, quantized resilient distributed observers are designed at the observer layer for each agent to guarantee the availability of observing the reference system under the influence of data-rate-constrained communication and DoS attacks. Based on the designed observers, smooth reference trajectories with the existence of high-order derivatives, which ensure the applicability of the backstepping technique, are then constructed at the decentralized controller layer, where a novel adaptive fractional-order prescribed performance-based backstepping quantized control strategy is proposed for high-order integer uncertain systems, resulting in an enhanced control scheme with the integration of fractional calculus. It is theoretically proven that under data-rate constraints and DoS attacks, the output tracking error of each agent in relation to the reference signal will constantly stay strictly within the predefined performance limit under the proposed control scheme, with all closed-loop signals remaining bounded. Simulation studies are provided as evidence to establish the efficacy of the proposed distributed quantized fractional adaptive prescribed performance-based control strategy.

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.

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