The rapid growth of distributed energy resources has led to a shift toward consumer-centered electricity markets, where peer-to-peer (P2P) energy trading plays a key role. While distributed market clearing algorithms enable scalable P2P trading, they often rely on direct information exchange between all trading partners, which raises concerns about privacy and high communication overhead. To address this, we propose a fully distributed market clearing algorithm that is updated using only information from its communication neighbors, regardless of transactions relationships. To further preserve privacy, we design an enhanced version (PF-DMCA) by incorporating a lightweight privacy mechanism based on state perturbation and channel weakening. Simulation results show that both algorithms achieve convergence to a solution with less than 0.09% deviation from the optimal social welfare. Moreover, compared to existing methods, the PF-DMCA requires 85.8% of communication times to achieve convergence, while F-DMCA requires only 50.4%, demonstrating superior communication efficiency.

This paper focuses on the issue of Nash equilibrium seeking with exponential convergence. A distributed algorithm with a two-layer adaptive structure is first proposed. Incorporating a damping term into the conventional adaptive law ensures that the parameter growth is not excessive, thereby improving its applicability in practical engineering systems. To further reduce the communication burden among players, a single-layer structure algorithm based on a dynamic event-triggered scheme is developed, where parameter updates are independent of global information, offering greater flexibility and scalability while effectively avoiding Zeno behavior. Theoretical analysis demonstrates that the proposed algorithm ensures exponential convergence of the players’ strategies to the Nash equilibrium. A numerical simulation further confirms the efficiency of the presented method and the accuracy of the theoretical results.

Prescribed-time stable systems frequently suffer from infinite gain issues that lead to practical infeasibility. This paper investigates the problem of stabilization for uncertain Euler-Lagrangian systems and proposes a singularity-free prescribed-time stabilization controller. Based on the time space deformation approach, the designed controller ensures singularity avoidance, effectively eliminating the existence of infinite gain. Besides, the considered Euler-Lagrangian systems are subject to unknown nonlinear functions and derivative-bounded external disturbances with unknown bounds. To achieve system stabilization under such uncertainty issue, this paper formulates multiple sliding manifolds with prescribed-time stability. The controller’s effectiveness is validated through simulation studies on a rendezvous formation problem.

This paper studies secure consensus of nonlinear multi-agent systems (MASs) affected by sequential scaling attacks and communication delays, employing an event-triggered delayed impulsive control strategy. Specifically, it considers sequential scaling attacks occurring within the communication channels between agents, while the communication delays arise in the controller-actuator pair. First, the attack properties include attack duration and attack frequency are defined. Then, a delayed impulsive control protocol that depends exclusively on neighboring agents’ state at event-triggered time instant is proposed to eliminate continuous control behavior. A sampled-data-based event-triggered mechanism (ETM) is introduced that uses the Lyapunov function at impulse time instant to determine communication intervals between agents, effectively reducing the need for continuous event detection. Furthermore, sufficient conditions for secure consensus of MASs are established, along with guidelines for designing event-triggering parameters. Finally, the effectiveness of the proposed approach is demonstrated via two numerical simulations.

This article investigates the problem of distributed Nash equilibrium (NE) seeking in noncooperative games within a directed communication network. For promoting the efficiency of communication among players, a gradient-based dynamic event-triggered mechanism is proposed, where Zeno behavior is excluded. Moreover, based on the Lyapunov stability theory, we derive sufficient conditions for exponential convergence and demonstrate that the seeking strategy proposed facilitates the convergence of players' actions toward the NE. To illustrate the effectiveness of the proposed strategies, simulation results are presented in a system consisting of five agents.

This article studies distributed Nash equilibrium seeking of a group of players based on an event-triggered mechanism under switching interaction topologies. First, a dynamic event-triggered function is proposed, which relies on the gradient of the player's payoff function and the triggering state information of its neighbors. It enables a rapid response to system changes and is able to effectively eliminate the Zeno behavior. Next, to maintain the strong connectivity in the directed graph for the links switched by the event-triggered mechanism at all times, a switching-triggered communication mechanism is designed in which triggered conditions are enhanced to include the instants of each player's link changes. Furthermore, by restricting switching frequency, this article establishes sufficient conditions with parameter ranges for exponential convergence to the Nash equilibrium. Finally, simulation results show the validity of the proposed strategy.

Many industrial parks, which are connected to the main grid, have integrated renewable energy to reduce carbon emission for achieving the goal of Industry 5.0. However, the optimal scheduling is challenging due to fluctuations in renewable energy generation. Hydrogen, which plays an important role in the future development of the power grid in Industry 5.0, offers an attractive option to coordinate with the batteries. This work focuses on the day-ahead scheduling of a hydrogen-based microgrid for an industrial park. A day-ahead scheduling model is established by taking into consideration the detailed nonlinear energy conversion behavior of the electrolyzer and fuel cell, as well as the two-timescale property of a battery energy storage system (BESS) and the hydrogen system, including an electrolyzer, a hydrogen energy storage system (HESS), and a fuel cell. Note that the optimization problem is a mixed integer nonlinear programming, which is challenging to be solved. A novel multilearning rate reinforcement learning algorithm is proposed and its convergence is also proved based on two-timescale stochastic approximation theory. Simulation results, based on real-world traces in Belgium at a 15-min resolution, are presented, which shows that the proposed method has a higher reward, lower-operating costs and less computing time. It is also found that the shorter scheduling period for the BESS can lead to reduced operating costs by decreasing the required purchasing power and the renewable energy curtailment power.

Differentially-private distributed optimization algorithms can preserve the private gradient but bring unavoidable trade-off between privacy and optimality. Some methods have been studied to address this based on diminishing stepsize with only sublinear convergence. To improve convergence rate, the exact Differentially-Private algorithm based on the distributed gradient Tracking and distributed Nesterov gradient methods (eDP-TN) with fixed stepsize is proposed to achieve privacy and the accelerated linear convergence to the optimal solution. Leveraging matrix spectrum and Laplace distribution, the linear convergence is established in mean. The privacy level of eDP-TN is also determined based on differential privacy over a finite time horizon. Simulations are conducted on a distributed sensor problem to verify the effectiveness of the theoretical findings.

This article is concerned with cluster consensus control of multiagent systems (MASs) with the fixed/switching topology under a dynamic event-trigger (DET) mechanism. A refined sampled-data-based DET scheme is proposed by introducing two dynamically adjusting threshold parameters to distinguish the different transmission requirements for neighboring agents intra and outer cluster. Faced with the difficulties of acquiring full state information among spatially distributed agents, output feedback is employed to construct cooperative control protocols. Both fixed and switching topologies are considered to execute the designed DET-based cooperative cluster consensus control protocols. By constructing appropriate Lyapunov-Krasovskii functionals (LKFs), some sufficient criteria in terms of matrix inequalities for the cluster consensus of MASs are derived, which can ensure that the error system with the proposed DET-based control strategy is asymptotically stable. Facing the nonconvex issue induced by output feedback, a particle swarm optimization (PSO)-based control design algorithm is novelly developed to calculate the control gains and event-triggering parameters jointly based on the derived stability criteria. The elements of the matrix variables are valued stochastically in certain ranges and the fitness function is designed as the accumulation of the weighting value of each matrix inequality. Finally, an application of multiple satellites formation flying is applied to numerically illustrate the effectiveness of the cluster consensus control strategy with the designed DET mechanism.

This article is concerned with the secure fully distributed event-triggered consensus problem of general linear multiagent systems subject to distributed sequential scaling (DSS) attacks. First, a generic DSS attack model is proposed, which enables different attack strategies in terms of scaling factors, attack frequency, and duration to be incorporated in various communication channels. Different from existing attack models, the DSS attack is a kind of scaling attacks in which attack sequences are characterized by the sequential attacks and with distributed attack strategies for different attack objectives. To resist the adverse effects of such DSS attacks and further reduce the unnecessary communication consumption of each communication channel, a channel-based dynamic event-triggered mechanism is next presented. Moreover, a fully distributed event-triggered consensus control protocol is developed such that the dependence of any global information of the network topology can be eliminated. Formal analysis criteria on the asymptotic convergence of the resultant consensus errors and Zeno-freeness are then derived, where the relationship of the triggering parameters, distributed scaling factors, and attack constraints is explicitly expressed. Furthermore, an offline algorithm without any global information is provided to determine both the adaptive consensus protocol gain matrices and the triggering parameters. Therefore, the parameters calculated by this algorithm are applicable to multiagent systems of different scales, which also confirms the flexibility and scalability of the proposed fully distributed event-triggered consensus control protocol. Finally, two simulation examples involving different scales of intelligent vehicles are given to validate the efficacy of the obtained theoretical results.

This article addresses the stabilization and synchronization problems of coupled neural networks (NNs) via an impulsive adaptive control (IAC) strategy. Unlike the traditional fixed-gain-based impulsive methods, a novel discrete-time-based adaptive updating law for the impulsive gain is designed to maintain the stabilization and synchronization performance of the coupled NNs, where the adaptive generator only intermittently updates its data at the impulsive instants. Several stabilization and synchronization criteria for the coupled NNs are established based on the impulsive adaptive feedback protocols. Additionally, the corresponding convergence analysis are also provided. Finally, the effectiveness of the obtained theoretical results is illustrated using two comparison simulation examples.

Distributed optimization achieves a minimized objective function through collaboration among distributed agents. Considering limited communication capabilities and privacy concerns, this article proposes a dynamic event-triggered differentially private gradient-tracking algorithm for distributed optimization. The communication requirement is reduced by event triggering, while the$\epsilon$-differential privacy is guaranteed by perturbations on states and the tracking of the average gradient. The convergence point is uniquely determined by the noise injected to the tracking. Sufficient conditions for stepsizes are established theoretically to guarantee the convergence in mean and almost surely. Moreover, the theoretical privacy level is rigorously obtained and the positive effect of the event-triggered communication on the privacy is also discussed. Simulations are conducted for the classification of the dataset on the stability of a 4-node star power system to verify the theoretical findings.

With the continuous development and progress of photovoltaic (PV) technology, the proportion of PV generation in microgrids has increased significantly, making microgrids more low-carbon and environmentally friendly. However, the high PV penetration can lead to excessive fluctuations in the exchange power at the point of common coupling (PCC), resulting in transmission line overheating and posing risks to the microgrid's stability. Aiming to address the active power fluctuations at PCC caused by high PV generation, the paper proposes a new cooperative dispatch algorithm based on multi-agent reinforcement learning. Our algorithm improves the training approach of dueling double deep Q-network by splitting the original reward function into a new auxiliary network. The auxiliary network guides the update direction of the main network during the training process, enabling the power system to mitigate power fluctuations while satisfying other physical constraints. Agents collaboratively learn optimal power dispatch strategies for hydrogen storage systems via localized communication with neighboring agents. We compare our proposed algorithm with baseline algorithms, and simulation results demonstrate its superior performance in mitigating active power fluctuations at PCC and maintaining stable load status for hydrogen energy storage systems.

This paper aims to study mean-square exponential consensus of nonlinear leader-following multi-agent systems (MASs) in the presence of noise via distributed random impulsive control. The noise is driven by a second-order moment process with bounded mean power. A distributed random impulsive controller whose impulses occur at random moments is designed to ensure the desired consensus performance while enhancing security of the system. Sufficient conditions in terms of the network topology and impulsive parameter are derived, using the Lyapunov-based approach and properties of stochastic process. Finally, a numerical example is presented to demonstrate the effectiveness of our theoretical results.

In multi-agent systems with leader-follower dynamics, choosing a subset of agents as leaders is a critical step in achieving the desired coordination performance. In this study, by considering consensus tracking for general linear multi-agent systems under switching topologies, we address the problem of selecting a minimum-size set of leaders by leveraging the submodularity ratio. First, using the dwell time technique, a criterion is derived to ensure that the states of all agents can converge to a reference trajectory that is directly tracked by each leader. Second, exploiting the derived consensus tracking criterion, the metrics with a structure of the Euclidean distance between specific vectors and the space spanned by an iteratively updated matrix are established to identify a set of leaders, and then the corresponding bound of the submodularity ratio is proposed. Third, combining the derived criterion and the constructed metrics, a leader selection scheme is presented together with three polynomial-time algorithms, and the related provable optimality bound of each algorithm can be obtained by leveraging the proposed bound of the submodularity ratio. Finally, illustrative examples are provided to verify the effectiveness of the proposed leader selection scheme.

This article is mainly concerned with the asynchronous control problem of fuzzy singularly perturbed systems (FSPSs) by designing an effective dynamic event-triggered control (ETC) strategy. Different from the existing results, a sample-based dynamic event-triggering condition related to the singular perturbation parameter (SPP)$\varepsilon$is proposed. Particularly, an auxiliary variable is employed in the event-triggered mechanism, which changes with the fluctuation of the system state. By constructing a novel parameter-dependent Lyapunov functional, some sufficient conditions are derived to stabilize FSPSs and solve the$\varepsilon$-dependent control gain. Furthermore, the upper bound of SPP$\bar{\varepsilon }$related with the proposed control gain is determined. The Zeno behavior can be naturally avoided by applying the sample-based dynamic ETC. Finally, two examples including a nonlinear circuit are presented to validate the effectiveness of obtained results.

Due to the close relevance to the reliability and efficiency of power systems, network parameters such as branch admittance have been the target of various cyberattacks. However, existing attack models are generally based on the impractical assumption that attackers can directly modify the data of network parameter stored in well-secured control centers. This article proposes a practical attack model and designs an optimal strategy to detect malicious modification of critical network parameters. Specifically, the vulnerability of network parameter error processing is discovered and exploited to indirectly modify the data of network parameter without accessing to the well-secured control center. A model of false-data-injection-enabled network parameter modification is proposed, which significantly reduces the requirements on attackers’ capability and system information. An optimal detection strategy is designed based on the analysis of the minimal protection set at a single branch, which can significantly reduce the number of protected measurements in detecting malicious modification of critical network parameters. Finally, numerical simulations are carried out on the PJM 5-bus and the IEEE 118-bus test systems to validate the theoretical results.

This paper is concerned with the generalized Nash equilibrium (GNE) seeking problem of noncooperative games in dynamic environments, where the cost function and coupled constraint of each agent are time-varying. In this case, each agent is required to make a decision before obtaining its cost function and a local inequality constraint. The purpose of the addressed problem is to establish a new distributed primary-dual and mirror descent online algorithm with inertia that is capable of seeking the GNE via time-varying communication graphs and has the potential of achieving a low average regret. Then, two information transmission modes and two estimate update strategies are discussed, respectively. Finally, a simulation example is presented to illustrate the effectiveness of the algorithms, and to further compare their performances.

This paper studies the dynamic event-triggered tracking control of a nonholonomic mobile robot. Firstly, a non-holonomic dynamic model for a mobile robot is suggested. Then, a dynamic event-triggered control strategy with an auxiliary dynamic parameter is proposed for the mobile robot to track the reference trajectory. Moreover, it is proved that the triggering time sequence does not exhibit the Zeno behavior. Finally, the effectiveness of the proposed control mechanism is validated by a numerical example.

This paper addresses a secure event-triggered consensus control problem for a class of nonlinear leader-following multi-agent systems (MASs) under denial of service (DoS) attacks and deception attacks. The novelty of this study lies in the development of a secure event-triggered impulsive control (ETIC) method that can effectively deal with the effects of random DoS and deception attacks, while also achieving the desired resource-efficient consensus control performance. More specifically, in order to save the previous communication resources, an event-triggered impulsive consensus control protocol is proposed to reduce data exchanges among the agents. Our further aim is to simultaneously guarantee the mean-square exponential consensus performance of the controlled MAS and achieve attack resilience as well as resource efficiency. Sufficient conditions on the consensus performance analysis are derived, and the lower bound of impulsive sequences is further specified to exclude the Zeno behavior. Finally, a numerical example involving a group of Chua’s circuits is given to illustrate the effectiveness of the derived theoretical results.

This paper explores the resilient design of petroleum refinery planning in response to disruption events. Based on an emerging quantitative measure of processing system resilience and three effective resilience enhancement strategies, a multi-objective two-stage adaptive robust mixed-integer fractional programming model is proposed to optimize the resilience economic objectives simultaneously under uncertainty of product price and the number of failed equipments after disruption events, which will guide the industrial production of the petroleum refining process.

This article investigates secure consensus of linear multiagent systems under event-triggered control subject to a scaling deception attack. Different from probabilistic models, a sequential scaling attack is considered, in which specific attack properties, such as the attack duration and frequency, are defined. Moreover, to alleviate the utilization of communication resources, distributed static and dynamic event-triggered control protocols are proposed and analyzed, respectively. This article aims at providing a resilient event-triggered framework to defend a kind of sequential scaling attack by exploring the relationship among the attack duration and frequency, and event-triggered parameters. First, the static event-triggered control is studied, and sufficient consensus conditions are derived, which impose constraints on the attack duration and frequency. Second, a state-based auxiliary variable is introduced in the dynamic event-triggered scheme. Under the proposed dynamic event-triggered control, consensus criteria involving triggering parameters, attack constraints, and system matrices are obtained. It proves that the Zeno behavior can be excluded. Moreover, the impacts of the scaling factor, triggering parameters, and attack properties are discussed. Finally, the effectiveness of the proposed event-triggered control mechanisms is validated by two examples.

Multiagent systems (MASs) provide an effective means for coordinating spatially distributed and networked agents (or nodes, subsystems) such that the desired cooperative tasks can be accomplished with promising reliability, manipulability, scalability, and efficiency. One key issue in the study of MASs is the design of distributed cooperative control protocol and algorithm that depend on only local and real-time information exchanges among interacting agents over networks. However, network-enabled information sharing and increasing connectivity in practical MASs present several attack factors for malicious adversaries, thereby rendering secure control of MASs fundamentally significant. This article provides a brief survey of systems and control technologies that have been available for addressing different secure control problems of MASs in the face of various malicious attacks. First, attacks on MASs are classified based on different configuration layers. Then, the existing attack models and strategies on communication layer and agent layer are systematically examined, respectively. Furthermore, some typical secure control techniques for MASs that have been employed to handle these attacks are surveyed. Finally, several challenging issues are envisioned for potential future research.

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and Internet of thing (IoT) devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. Theoretical proofs of the convergence of quantization factors, unbiasedness of FTTQ, as well as a reduced weight divergence are given. On the basis of FTTQ, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available data sets, and our results demonstrate that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data in contrast to the canonical federated learning algorithms.

Synchronization of a class of nonlinear singularly perturbed complex networks (SPCNs) with semi-Markov jump topologies and impulsive effects is studied in this article. A complex network with a kind of random switching topologies is considered, where the randomness is depicted by a semi-Markov chain. A method is put forward to obtain the upper bound of singularly perturbed parameter (SPP) with different coupling strengths, and the concept of average impulsive interval is introduced to regulate the frequency of impulses. By utilizing the SPP-dependent semi-Markovian Lyapunov function, some sufficient conditions are derived for achieving synchronization of an SPCN. The effectiveness and validity of the proposed synchronization strategy are verified by two numerical examples.

This article deals with the distributed$\mathcal {H}_{\infty }$robust control problem for linear multiagent systems perturbed by external disturbances and norm-bounded uncertain parameters over the Markovian randomly switching communication topologies. To tackle this problem, the distributed observer-based controller is proposed, which requires the relative information between neighbors and the absolute information of a subset of the nodes, and thus is intrinsically distributed. It is of great interest to see that the distributed$\mathcal {H}_{\infty }$robust control problem governed by such a controller can be converted to the stabilization with$\mathcal {H}_{\infty }$disturbance attenuation problems of some decoupled linear systems, whose dimensions equal those of a single node. Then, the$\mathcal {H}_{\infty }$stochastic robust performance region is defined to indicate the robustness of this controller against the variation of communication topologies. It is theoretically shown that the distributed observer-based controller yields bounded and connected robust performance region. Finally, the theoretical results are verified by conducting numerical simulations and experiments.

This paper investigates secure consensus of two-timescale networks subject to a sequential scaling attack, in which constraints on the attack duration and frequency are given. Under this attack framework, a ε-dependent controller in a distributed manner is proposed. By virtue of the ε-dependent Lyapunov function, stability of the error system is analyzed and sufficient conditions for secure consensus are obtained. Furthermore, the corresponding upper bound εmaxis derived, which means secure consensus of two-timescale networks is ensured for ε ∈ (0, εmax]. Finally, the effectiveness of the proposed secure control protocol is validated by a numerical simulation.

This paper investigates the event-triggered bipartite consensus for linear multi-gent systems (MASs) subject to quantized communication on the basis of a connected structurally balanced signed graph. Firstly, one proposes a control strategy combined of logarithmic quantizer and a dynamic event-triggered strategy. Then, based on Lyapunov function approach, sufficient conditions for the bounded bipartite consensus of MASs with event-trigger and relative quantized state measurements are derived. Furthermore, the Zeno behavior is excluded for the triggering time sequences. Finally, simulation study is given to verify the effectiveness of the proposed dynamic event-triggered control strategy with quantized relative state measurements.

This paper addresses the issue of refinery production planning under uncertainty. A data-driven Wasserstein distributionally robust optimization approach is proposed to optimize refinery planning operations. The uncertainties of product prices are modeled as an ambiguity set based on the Wasserstein metric, which contains a family of possible probability distributions of uncertain parameters. Then, a tractable Wasserstein distributionally robust counterpart is derived by using dual operation. Finally, a case study from the real-world refinery is performed to demonstrate the effectiveness of the proposed approach. The results show that compared with the traditional stochastic programming method, the data-driven Wasserstein distributionally robust optimization approach is less sensitive to variations of product prices, and provides optimal solutions with better out-of-sample performance.

This article is concerned with quasi-synchronization of grid-connected systems in electrical networks, where heterogeneous Inductance-Capacitance (LC) oscillators are coupled via electrical inductance subject to time-varying delays. Note that complete synchronization fails to be accomplished due to the existence of nonidentical parameters and quasi-synchronization cannot be achieved via non-delayed inductive coupling. A configuration of multiple heterogeneous LC oscillators with inductive coupling subject to time-varying lumped delay is first constructed by introducing an active delay intentionally. Then the complete-type Lyapunov-Krasovskii functionals (LKF) are constructed to investigate the exponential convergence of quasi-synchronization of LC oscillators in the presence of parameter mismatches utilizing the positive effects of interval time-varying delays. Some feasible synchronization criteria are derived. The gain matrix can be designed by solving a set of linear matrix inequalities combining an optimization algorithm. Finally, a numerical example of five LC oscillators in photovoltaic grid-connected system is given to demonstrate the effectiveness of the proposed method.

This paper is concerned with position-based synchronization of networked harmonic oscillators. Note that synchronization cannot be achieved via current-position-based protocols. The objective of this paper is to investigate the positive effects of network-induced delays on the synchronization of networked harmonic oscillators. That is, if taking network-induced delays into account, the motion of harmonic oscillators can be really synchronized via a proper position-based control protocol. In doing so, the harmonic oscillators are connected via a shared digital communication network. Different from some existing results, system measurements from oscillator nodes are sampled in an asynchronous way; and network-induced delays are assumed to be time varying and bounded, and they do not need to be synchronous with those from the other communication channels. At each oscillator node, a buffer is embedded into the controller to store the newest sampled-data packets transmitted from its neighboring nodes through communication channels. Then, based on the store of the buffer, the controller computes its control signal with its own period. As a result, the overall synchronization error system is modeled as a linear system with multiple interval time-varying delays. By employing the discretized Lyapunov-Krasovskii functional method, a sufficient condition on synchronization of networked harmonic oscillators is derived, which can ensure that the synchronization error system is asymptotically stable for network-induced delays falling into a certain closed interval whose lower bound is a positive real number. This condition is thus used to design suitable control protocols in terms of linear matrix inequalities with several tuning parameters. Finally, a multirobot platform is given to demonstrate the effectiveness of the proposed method.

Quantized synchronization of master-slave neural networks with an event-triggered scheme under denial-of-service (DoS) attacks is studied in this paper. A kind of DoS attacks with constraints on attack frequency and duration is considered. Firstly, a quantized output feedback control scheme based on event-triggered strategy is proposed. Then a sufficient condition for secure event-triggered feedback control based on quantized output measurements is derived, from which the attack frequency and duration that the system can render is given. It is shown that the slave system is able to synchronize with the master system meanwhile the Zeno behavior can be excluded. The controller design is also included. Finally, an example is given to verify the theoretical results.

This paper studies event-triggered observer-based resilient control of linear multi-agent systems under false data injection attacks. It is assumed that false data is injected into the channel from the controller to the actuator. By considering the attack signal as the augmented state, event-triggered observers are designed to estimate system states and attack signals. Then observer-based resilient controller is proposed to counteract the impact of attacks. An sufficient condition is derived and the exclusion of Zeno behavior is discussed. One example is given to verify the feasibility of the proposed scheme.

This paper is concerned with event-triggered consensus of general linear multiagent systems (MASs) in leaderless and leader-following networks, respectively, in the framework of adaptive control. A distributed dynamic event-triggered strategy is first proposed, in which an auxiliary parameter is introduced for each agent to regulate its threshold dynamically. The time-varying threshold ensures less triggering instants, compared with the traditional static one. Then under the proposed event-triggered strategy, a distributed adaptive consensus protocol is formed including the updating law of the coupling strength for each agent. Some criteria are derived to guarantee leaderless or leader-following consensus for MASs with general linear dynamics, respectively. Moreover, it is proved that the triggering time sequences do not exhibit Zeno behavior. Finally, the effectiveness of the proposed dynamic event-triggered control mechanism combined with adaptive control is validated by two examples.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.

This paper investigates impulsive consensus of nonlinear multi-agent systems in the leader-following framework subject to denial-of-service (DoS) attacks. The case that the communication network suffers from DoS attacks is considered, which will destroy the communication link, resulting in switching topologies. Based on the assumption that the system can recover from DoS attacks, sufficient conditions on the design of the impulse interval and the impulse attack ratio that the system can sustain is provided. A numerical example is given to validate the effectiveness of our theoretical results.

This paper is concerned with H∞synchronization of coupled oscillators in a master-slave framework, in which the oscillators cannot be stabilized by nondelayed sampled position data, but can be stabilized by sampled position data with delays restricted by nonzero lower bounds and upper bounds. A configuration of networked master-slave oscillators with a remote controller is first constructed. Then the positive effects of delays on master-slave synchronization are investigated. Some delay-dependent H∞synchronization criteria are derived by constructing augmented discretized Lyapunov-Krasovskii functionals for determinate sampling and stochastic sampling, respectively. The controller can be designed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to verify the theoretical results. It is shown that the maximum allowable sampling period in the case of stochastic sampling is larger than the one in the case of determinate sampling. Stochastic sampling can also provide a tradeoff between network-induced delays and the sampling periods, enhancing the master-slave synchronization performance.

This paper is concerned with leader-following consensus of general linear multi-agent systems. First, to save the limited communication resources, a novel distributed event-triggered control strategy with state-dependent and time-dependent thresholds is delicately developed. Without requiring continuous communication among the following agents, the proposed strategy can allow for larger minimum inter-event times. Then, by employing the Lyapunov function method, bounded consensus for multi-agent systems is achieved. Moreover, it is proved that the triggering time sequence does not exhibit Zeno behavior. Finally, the effectiveness of the control mechanism is verified by a numerical example.

This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

This paper is concerned with leader-following consensus of second-order multiagent systems with nonlinear dynamics. First, to save the limited communication resources, a new event-triggered control protocol is delicately developed without requiring continuous communication among the follower agents. Then, by employing the Lyapunov functional method and the Kronecker product technique, a novel sufficient criterion with less conservation is derived to guarantee the leader-following consensus while excluding the Zeno behavior. Furthermore, for the first time, an algorithm to actively adjust the leader adjacency matrix is presented, which efficiently expands the application range of some existing criteria. An example is finally given to illustrate the effectiveness of theoretical results.

This paper is concerned with finite-time L2leader-follower consensus of networked Euler-Lagrange systems in the presence of external disturbances. A distributed finite-time L2control protocol is proposed by using backstepping design such that a group of follower agents modeled by Euler-Lagrange systems can follow a desired leader agent and achieve leader-follower consensus in finite time. Moreover, the finite-time L2gain is less than or equal to a prescribed value. A simulation example of a network composed of seven two-link manipulators is given to show the effectiveness of the theoretical results.

This paper addresses the attack frequency estimation for networked control systems suffering from denial of service (DoS) attacks. First, the networked control systems (NCSs) subjecting to DoS attacks are modeled as switched systems between normal systems and attacked systems. Secondly, by considering the worst scenario of DoS attacks with energy constraints, the maximum allowable update interval is well characterized under consideration of the limitation of the event-triggered control scheme and discrete sampling. Then, by use of Lyapunov theory, a stability criterion is derived to obtain the maximum number of DoS attacks while ensuring the globally exponentially stable of studied system. Finally, an example is used to show the validity of proposed results.

This paper is concerned with sampled-data leader-following consensus of a group of agents with nonlinear characteristic. A distributed consensus protocol with probabilistic sampling in two sampling periods is proposed. First, a general consensus criterion is derived for multiagent systems under a directed graph. A number of results in several special cases without transmittal delays or with the deterministic sampling are obtained. Second, a dimension-reduced condition is obtained for multiagent systems under an undirected graph. It is shown that the leader-following consensus problem with stochastic sampling can be transferred into a master-slave synchronization problem with only one master system and two slave systems. The problem solving is independent of the number of agents, which greatly facilitates its application to large-scale networked agents. Third, the network design issue is further addressed, demonstrating the positive and active roles of the network structure in reaching consensus. Finally, two examples are given to verify the theoretical results.

This paper is concerned with the synchronization of multiagent systems connected via different types of interactions, known as multilayer networks. Additive coupling and Markovian switching coupling are proposed to capture the layered connections with two kinds of mathematical models constructed. First, based on simultaneously diagonalization of multiple Laplacian matrices, a general criterion is derived, ensuring that the synchronization problem with additive coupling can be decoupled. Then, an alternative condition is presented, which is related to the number of layers, regardless of the number of agents. With the derived criteria, a concept of joint synchronization region is introduced and further discussed as a network design problem. Synchronization with Markovian switching layers is also analyzed in parallel, exemplified by some special cases of two-layer networks. Finally, a group of cellular neural networks coupled by two-layer connections are chosen to illustrate the effectiveness of the theoretical results.

Quasi-synchronization of heterogenous dynamic networks is studied by using impulsive control in this paper. The asymmetric network connections are considered. First, the weighted average state is introduced as the virtual leader. By defining the synchronization error between the virtual leader and the network node, impulsive quasi-synchronization is analyzed and a criterion is derived to ensure quasi-synchronization in the delayed heterogenous network. Then delayed networks with symmetric connections and delay-free networks are studied, respectively, with simpler conditions obtained. Numerical simulations demonstrate the effectiveness of the derived results.

This paper investigates consensus of nonlinear multi-agent systems with stochastic disturbances. By sampling signals from the leader agent at discrete instants, leader-following consensus in the mean square is achieved based on the theory of Ito stochastic differential equations and Lyapunov-Krasovskii functional stability theory with a sufficient condition derived. Then two special cases: 1) the transmittal delay is very small, which can be approximately regards as zero; 2) the interconnections of agents are undirected are discussed, respectively. Finally, an example is given to verify the effectiveness of the theoretical results.

This paper studies global synchronization between a heterogeneous dynamical network and a known target trajectory via distributed impulsive control. Synchronization with an error level, called quasi-synchronization, is analyzed by utilizing the time-varying Lyapunov function. Some sufficient quasi-synchronization conditions are presented and explicit expressions of error levels are derived. Furthermore, the effects of the pinning control matrix and the coupling strength are explored. Unlike the continuous pinning feedback control, it is shown that a large coupling strength will destroy synchronization in the present of impulsive controller, which is also verified by our simulations.

This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.

This paper investigates the problem of master-slave synchronization of two delayed Lur'e systems in the presence of parameter mismatches. First, by analyzing the corresponding synchronization error system, synchronization with an error level, which is referred to as quasi-synchronization, is established. Some delay-dependent quasi-synchronization criteria are derived. An estimation of the synchronization error bound is given, and an explicit expression of error levels is obtained. Second, sufficient conditions on the existence of feedback controllers under a predetermined error level are provided. The controller gains are obtained by solving a set of linear matrix inequalities. Finally, a delayed Chua's circuit is chosen to illustrate the effectiveness of the derived results.

This paper investigates exponential synchronization of coupled networks with hybrid coupling, which is composed of constant coupling and discrete-delay coupling. There is only one transmittal delay in the delayed coupling. The fact is that in the signal transmission process, the time delay affects only the variable that is being transmitted from one system to another, then it makes sense to assume that there is only one single delay contributing to the dynamics. Some sufficient conditions for synchronization are derived based on Lyapunov functional and linear matrix inequality (LMI). In particular, the coupling matrix may be asymmetric or nondiagonal. Moreover, the transmittal delay can be different from the one in the isolated system. A distinctive feature of this work is that the synchronized state will vary in comparison with the conventional synchronized solution. Especially, the degree of the nodes and the inner delayed coupling matrix heavily influence the synchronized state. Finally, a chaotic neural network is used as the node in two regular networks to show the effectiveness of the proposed criteria.