This paper presents a prescribed-time optimal resilient control strategy for multiquadrotor systems under composite attacks, including sensor attacks and denial of service (DoS) attacks. To counter the sensor attacks, a distributed resilient extended state observer, integrated with a sensor attack compensator, is proposed for each follower to estimate its own states and the unknown lumped disturbances. Furthermore, a prescribed-time fully distributed resilient observer is constructed to provide each follower with the estimation of the reference signals even under DoS attacks. Based on these estimations, a prescribed-time optimal controller is designed by combining a novel coordinate transformation and the experience replay technique. The stability analysis is presented to verify that the attitude tracking error can converge to a small neighborhood of zero within a prescribed time. Finally, a simulation example is given to validate the effectiveness of the proposed framework.

This paper proposes inverse reinforcement learning (IRL) algorithms to solve the optimal synchronization problems for multi-agent systems (MASs) subject to disturbances. A framework of expert-learner MASs is developed to reconstruct the unknown cost functions of expert MAS. Within this framework, the learner MAS imitates the behavior trajectories of expert MAS under observation. We first present a model-based IRL algorithm including two iteration loops: an inner loop on optimal control and an outer loop on inverse optimal control. Moreover, a modelfree IRL algorithm is further proposed using the observed input and state data without requiring the dynamics knowledge of two MASs. Then generalized fuzzy hyperbolic models are utilized to implement the model-free algorithm, and we provide the convergence and stabilization proofs of our proposed algorithms. Finally, the effectiveness of the proposed algorithms is verified through a simulation example.

This paper presents a distributed data-driven inverse reinforcement learning (IRL) framework for multiagent systems using state trajectories only. By exploiting the state observations, the approach achieves optimal consensus among agents while simultaneously inferring unknown cost functions of all agents. Concurrent learning-based adaptive laws and parameter estimators are designed to estimate feedback control gains and unknown system’s dynamics in real time, which mitigates the requirement for the persistency of excitation condition. Building on these learned models, we develop a distributed data-driven IRL approach and analyze the ultimate boundedness of both the weight estimate errors and local neighbor consensus errors. Finally, the proposed approach is validated through simulations on a DC islanded microgrid.

The carbon-factor accounting method is widely used for carbon emissions (CEs) evaluation and carbon quotas (CQs) allocation in integrated energy systems (IESs). However, its linear mapping model cannot capture the real-time influence of external and environmental factors, which weakens the constraint effect of CQs on CEs and limits the overall energy–carbon optimization capability. To address this issue, this article proposes a large language model (LLM)-assisted deep reinforcement learning (DRL) optimization method to enhance the constraint effect of CQs on CEs in IESs. First, a nonlinear CQ modeling method based on LLM semantic reasoning is proposed, breaking the dependence of the linear carbon-factor method on expert experience. Second, considering information including energy structure, market changes, policy orientation, and environmental constraints, an interpretable nonlinear CQ accounting method is designed based on LLM to enhance the constraint effect of CQs on CEs. Finally, a trigger mechanism is designed to achieve collaborative optimization through automatic interaction between LLM and DRL. Simulation results indicate that the optimized CQ mechanism enforces a more effective constraint on CE behaviors, enabling timelier response and enhanced energy–carbon optimization performance.

The rapid expansion of Electric Internet of Things (EIoT) systems has led to an increased demand for accurate and secure methodologies capable of handling short-term energy information forecasting, which is crucial for optimizing energy management and maintaining smart grid stability. Current federated learning (FL) frameworks, however, struggle to cope with the inherent heterogeneity of devices and the constrained communication environments typical in EIoT settings. To overcome these challenges, we introduce the federated heterogeneity-aware adaptive framework (FedHA), a novel approach that combines an adaptive asynchronous federated aggregation (AAF) framework with a hierarchical knowledge distillation (HKD) strategy. The AAF framework reduces global model aggregation time and minimizes the impact of straggling clients by enabling parallel training and dynamically adjusting learning rates based on client participation. Concurrently, HKD improves the stability and consistency of model convergence, particularly in non-IID data scenarios, by distilling and leveraging knowledge from clients that most accurately represents the global gradient direction. Furthermore, we developed FedHAL, a light-weight version of FedHA, which optimizes communication efficiency, making it ideal for deployment in resource-constrained edge environments. Extensive experiments on real-world EIoT datasets confirm that FedHA outperforms existing FL algorithms in terms of both prediction accuracy and adaptability, underscoring its potential for widespread application in dynamic and diverse EIoT contexts.

This paper investigates a privacy-preserving consensus tracking problem for a class of nonstrict-feedback discrete-time multi-agent systems (MASs). An improved Liu cryptosystem is developed to alleviate the errors between encryption and decryption on the plaintext, which ensures satisfactory recovery of the plaintext information. A reinforcement learning (RL) technique is then employed to compensate for unknown dynamics and errors between true signals and decrypted ones. Based on the backstepping and graph theory, an RL-based privacy-preserving consensus tracking control strategy is further designed. By virtue of graph theory and Lyapunov stability theory, it is shown that the consensus tracking errors and all signals in the MAS are ultimately bounded. Finally, simulation examples are presented for verification of the effectiveness of the control strategy.

Since the continuous progress of Internet technology and social networks, content sharing on social platforms that reflects personal feelings and emotions has proliferated. Consequently, the study of people’s emotions has gained considerable popularity because of the expanding use of social media, which has provided enormous data for data-driven research and more attention on the psychological health of people nowadays. To deepen the analysis of people’s emotions on the internet, Multimodal Sentiment Analysis(MSA) combines multiple modalities such as texts, images, and sounds to comprehensively analyze and assess an individual’s emotional state. However, ignoring the relationship between different modalities, most of the previous multimodal sentiment analysis models were limited to feature extraction of a single modal that cannot precisely predict object’s state of mind. In this article, we propose a framework called Mutual Infomax Utterance Representation (MIUR) which draws on the concept of mutual information in information theory and introduces an information exchange module between modalities, effectively filters out task independent random noise while preserving shared information across modalities as much as possible. We conducted experimental verification on the publicly available popular sentiment datasets MOSI and MOSEI, and the results showed that our model demonstrated significant advancements in contrast to existing advanced models.

This study considers the finite-time prescribed performanceH∞tracking consensus control problem in nonlinear multi-agent systems with time-varying disturbance and designs a distributed event-triggered control strategy. First, a class of time-dependent error performance constraint functions is introduced to effectively address the negative impact of disturbances on the transient and steady-state performance of a system. Second, a dynamic event-triggered protocol defined using periodic sampling data is proposed to reduce the information transmission between agents and decrease the controller’s update frequency. Third, the designed dynamic event-triggered mechanism is combined with the finite-timeH∞control theory, and sufficient conditions for the system to achieve tracking consensus in a finite time are obtained by employing the Lyapunov stability theory and linear matrix inequality. Finally, the feasibility of the proposed distributed control strategy is verified through numerical simulations and practical examples.

The coordinated operation of grid-interactive buildings contributes to creating a more resilient and reliable power grid. However, existing studies fail to identify the demand changes of each building resulting from their coordinated participation in providing grid services, which affects the economic compensation of each building and its willingness to coordinate. In this article, we investigate an optimal coordinated operation problem for grid-interactive residential buildings (GRBs) while considering generation capacity services and economic compensation for participating GRBs. Specifically, we first formulate two optimization problems to capture the different objectives of GRBs during non-service periods and grid-service periods, respectively. Then, we develop a physically consistent neural network (PCNN)-assisted hierarchical model predictive control (HMPC)-based GRB energy management algorithm to solve the optimization problem during non-service periods. Next, we propose a coordinated operation algorithm to solve the optimization problem during grid-service periods based on PCNN-assisted HMPC and rule-assisted binary search. By comparing the initial solutions from the proposed energy management algorithm with the final solutions generated by the proposed coordination algorithm, the demand changes of each GRB during service periods can be identified. Simulation results indicate that the proposed coordination algorithm achieves up to 37.8114% lower energy costs and 82.1459% better grid service performance than benchmarks while maintaining high thermal comfort. Note to Practitioners—Buildings with distributed energy resources (e.g., solar generation, energy storage) and flexible loads (e.g., heating, ventilation, and air conditioning (HVAC) systems) have significant potential to provide grid services, such as voltage support, frequency regulation, and power peak reduction. Since a single residential building contributes minimally to service quality, multi-building coordination through a trusted third party is necessary. However, since participation in providing grid services may affect occupant comfort and building energy costs, the demand change of each residential building should be identified so that the corresponding economic compensation can be calculated, which is a key factor for the successful deployment of such grid-interactive residential buildings (GRBs). To this end, we develop an optimal energy management algorithm for each residential building during non-service periods, which aims to minimize building energy cost while maintaining high occupant comfort. Based on the developed energy management algorithm, a coordinated operation algorithm for service periods is further proposed to limit the peak demand below a value predetermined by the system operator. By comparing the initial decisions from the energy management algorithm with the final decisions generated by the proposed coordination algorithm, we can identify the demand change of each building during service periods. Numerical results demonstrate that the proposed coordinated operation algorithm can help participating GRBs reduce energy costs and enhance service performance for power grids, with negligible sacrifice to occupant comfort.

Due to dynamic characteristic of demand response and stochastic nature of power generation, it brings great challenge to smart energy management. In this paper, a demand response model is created with two-level dynamic pricing transaction among grid operator, service provider and customers, which also involves customers’ active participation with load shifting issue. To effectively control system load on the demand side, an improved deep reinforcement learning approach is proposed with a recursive least square (RLS) technique to deal with the dynamic pricing demand response problem, which accelerates the on-line training and optimization efficiency. On the power generation side, a probabilistic penalty-based boundary intersection (PBI) based multi-objective optimization algorithm is improved to optimize the economic cost, emission rate and statistic voltage stability index (SVSI) simultaneously with generated stochastic scenarios, which can ensure energy conservation and environmental protection, as well as system security. The case results reveal that the proposed two-level optimization strategy successfully deals with energy management with dynamic pricing demand response.Note to Practitioners—This paper is motivated by solving stochastic energy management issue of isolated power system with dynamic pricing demand response. Those existing methods merely focus on the load demand or power generation side, and the methods for demand response issue lacks efficient on-line learning ability, while this work proposes a recursive least square based deep reinforcement learning approach to tackle with the two-level dynamic pricing demand response issue, scenario based PBI multi-objective optimization is proposed to solve the power dispatch issue on power generation side, and the numerical analysis results suggest that the proposed optimization strategy can deal with the whole energy management issue well. The future work will focus on the dynamic power-load coordination in the energy management issue.

In order to improve bus voltage profile in active distribution networks with high penetration of photovoltaics (PV) and electric vehicle (EV), this paper proposes an event-triggered multi-time-scale regulation method. Under a designable triggered mechanism driven by changes in power flow, a sensitivity-based centralized hybrid power compensation of PV and EV is used to alleviate long-term voltage offset throughout trigger intervals. Then, a dispersed local power compensation of PV or EV is additionally used to reduce short-term voltage fluctuation at involved nontrigger instants. Besides, its comprehensive performance is analyzed by establishing quantitative index, which can in turn be used to design trigger parameter. Finally, the effectiveness of this method is verified through case study in a real scene.

This study proposes an active defense strategy against denial-of-service (DoS) attacks to address the secure event-triggered control of multiarea load frequency control (LFC) systems. A novel interval secure event-triggered mechanism (ISETM) is introduced, integrating event-triggered control with cybersecurity mechanisms under the software defined network (SDN) framework. ISETM generates not only a triggering instant but also a secure triggering interval (STI) simultaneously. The STI sent to the SDN control plane is an estimation time interval generated by the Taylor expansion and model-based prediction method. During this interval, the SDN control plane programs OpenFlow switches to filter attack traffics, ensuring delayed but secure triggering transmission. Under ISETM conditions represented by two systems of inequalities, a multiarea LFC system is modeled as a delay system incorporating a triggering error based on the Taylor expansion. To achieve performance of the established LFC system, a criterion is derived using the Lyapunov-Krasovskii functional method. A codesign approach is provided to solve the proposed ISETM control (ISETC) gains through linear matrix inequality (LMI) techniques. Finally, simulations validate the effectiveness and advantages of our proposed method.

In this article, we consider the problem of privacy preservation in consensus control for second-order integrator multiagent systems (MASs). Specifically, we consider the setting where the initial position and velocity of each legitimate agent are both private, an internal or external adversary wants to identify them based on the information it obtains. To deal with this scenario, we propose a privacy preservation algorithm based on a position and velocity simultaneous perturbation technique. To be specific, our algorithm consists of a collaborative scrambling phase and a convergence phase. In the scrambling phase, each agent is required to produce two sets of edge-based perturbation signals that are, respectively, imposed on the local position and velocity signals before transmission, with the purpose of preserving privacy; in the convergence phase, each agent updates its state per a normal rule, aiming to achieving accurate consensus. Also, we establish a system-theoretic framework to analyze privacy performance by examining the indistinguishability of private values' arbitrary variations to adversaries, and further show that, an internal adversary cannot infer the privacy of a legitimate agent provided it has at least one legitimate in-neighbor or out-neighbor, and the privacy is leaked out once that agent exclusively connects to the internal adversary in bidirectional way. As for external eavesdroppers, they can never infer any agent's privacy if the gain parameters in the scrambling phase are not accessible to them. Finally, two simulation examples illustrate the validity of the proposed approach.

This study considers the periodic event-triggered prescribed tracking problem for stochastic nonlinear systems, whose output is available only at sampling time. With the limited sampled data of output, a state observer via neural-network approximation is constructed to estimate the unmeasurable states, and then a novel event-triggered mechanism is designed by monitoring the estimated states at sampling time to avoid the continuous communication. The negative deviation effects between the event-triggered controller and the continuous controller are eliminated by introducing two intermediate sampling deviation terms. Moreover, a performance function is introduced to achieve more flexible tracking performance. This function represents different performance behaviors and addresses the issue of redesigning controllers. By determining an allowable sampling period, it is proven that all states of the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error satisfies a flexible prescribed performance. Finally, two examples verify the effectiveness.

The tracking control problem for strict-feedback systems with unknown dynamics has been extensively studied. However, most existing control approaches require online approximation models and associated a priori assumptions. In order to avoid the necessity of deriving online models, this article proposes a data-driven backstepping control (DBC) approach for a class of strict-feedback systems with unknown dynamics. First, unlike the widely-studied adaptive backstepping control approaches, we identify the unknown dynamics of each subsystem based on off-line data and develop a data-driven continuous-time Lyapunov equation return controller, ensuring semi-global exponential stability of the error system. Furthermore, we propose a data-driven dynamic surface control (DDSC) approach for the "complexity explosion" problem in DBC. This approach uses a data-driven linear matrix inequality to return the controller, ensuring that the error system remains semi-globally ultimately uniformly bounded, even when the derivative of the virtual controller cannot be calculated. Finally, the superiority and effectiveness of DBC and DDSC are verified by simulation examples.

To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply-demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.

This paper proposes a novel co-design method of denial-of-service (DoS) attack-resilient time varying estimators/observers and controllers aimed at addressing these challenges in a discrete-time linear networked control systems (NCSs) under sensor and actuator false data injection (FDI) attacks. Our time-varying estimators have uniquely equipped to perform a joint estimation of the system state, sensor and actuator attack signals despite the presence of the intermittent DoS attacks. The main features of the developed time-varying observers are twofold: firstly, it involves time-varying gains corresponding to the DoS attack off/on transitions, and secondly, it entails augmenting the estimations of sensor and actuator FDI attacks within the piecewise observer error dynamics, thus providing a comprehensively and rigorous estimation and control methodologies. By introducing the conceptions of minimum and maximum sleeping/active durations of DoS attacks, a new time-varying DoS attack instant-dependent piecewise Lyapunov function approach is proposed to analyze theH∞stability of the augmented estimation error system and closed-loop control system under sensor and actuator FDI attacks. Based on the obtainedH∞stability analysis results, time-varying gains of state and attack observers are formulated to construct the DoS-resilient observers. Besides, the time-varying feedback gains are also obtained to construct the DoS-resilient controllers by attack compensation based on the actuator FDI attack estimations of time-varying observers, thus achieving the mitigation of sensor and actuator FDI attacks. Case studies are performed on a three-area interconnected power systems under DoS and FDI attacks to validate the effectiveness and advantages of the developed theoretical findings.

Uncertainty aggregation characterization of multi spatial–temporal distributed energy resources (DERs) is crucial for effective decision-making and control in power systems. In this article, we propose a cloud-edge-end collaboration approach to quantify the aggregated uncertainty of power generation from multi spatial–temporal DERs. First, considering the temporal dynamic and electrical topology correlation of DERs, a local uncertainty aggregation model based on a spatial-temporal graph neural network (STGNN) is developed. This model can effectively extract the spatial-temporal characteristics of data. Second, addressing the data silo problem caused by the unwillingness of various stakeholders managing the DERs to share data due to privacy concerns, an uncertainty aggregation model training mechanism based on an adaptive secure federated learning is proposed. This mechanism enables collaborative modeling of uncertainty aggregation models across stakeholders while preserving user privacy. In addition, it improves the quality of local model training by adaptively extracting parameter information from the global model for local model initialization. Moreover, since the probability distribution of the aggregated uncertainty is unknown, this article combines STGNN with the weighted quantile regression model to characterize the aggregated uncertainty without prior assumptions about the distribution, and by assigning differentiated weights to aggregation results under different confidence levels based on their importance, the proposed method can better meet the diverse needs of power grid. Finally, simulations conducted on the IEEE 33-bus system and IEEE 69-bus system validate the effectiveness of the proposed method.

The intermittency of solar irradiance is the main cause of rapid fluctuations in the power output of photovoltaic (PV) systems. These fluctuations hinder the large-scale integration of solar power generation equipment into the grid, which in turn hinders the process of utilizing solar energy resources to reduce carbon emissions. The main way to solve this dilemma is to achieve high-precision forecasting of solar irradiance. Although various methods exist to forecast the variations of solar irradiance, few focus on fully utilizing multimodal data information and fuzzy method to improve the forecasting performance. Therefore, a forecasting method combining multimodal data fusion and fuzzification is proposed to forecast ultra short-term global horizontal irradiance (GHI). First, a modal conversion method is designed to convert temporal modal data to spatial modal data. Then, the fused data are formed by fusing the converted data with normal and under exposure all-sky images. Subsequently, the fuzzy method is used to generate fuzzy GHI data with low nonlinear features. Last, we utilize deep neural networks to learn potential patterns between fused data and fuzzy GHI data in an end-to-end manner. Our method has been comprehensively validated on data provided by the National Renewable Energy Laboratory, demonstrating its effectiveness, and achieving the highest forecasting accuracy compared to state-of-the-art methods.

Integrated energy system (IES) facilitates efficient energy conversion and utilization. However, the joint optimization of energy use and carbon emissions (CEs) remains a significant and widely recognized challenge in this field. In this article, to solve the problem, a novel decision-making framework is proposed with leveraging a large language model (LLM) to assist deep reinforcement learning (DRL). First, a dynamic priority trading strategy is designed based on real-time supply and demand, which is adjusted dynamically through a trading matrix. Furthermore, a bidirectional equilibrium pricing mechanism is designed to determine reasonable prices that balance the interests of trading parties. Finally, the powerful inference and analysis capabilities of the LLM are leveraged to optimize DRL algorithms through interactive iterations and feedback loops, thereby enhancing decision-making performance. The experimental results demonstrate that the improved algorithm outperforms the baseline algorithm in terms of cost control, CE limitation.

This article presents a cryptography-based event-triggered resilient distributed charging scheduling method for plug-in electric vehicles (PEVs), addressing non-ideal factors such as privacy leakage, DoS attacks, and constraints on communication and computational resources. The proposed approach addresses two key challenges: first, integrating an edge-event-triggered mechanism (EETM) into the scheduling algorithm to reduce the communication and computational overhead caused by the cryptography-based confidential communication protocol (CCP), whose execution significantly increases transmitted data size and requires substantial computational resources; second, ensuring that the EETM-based regulation algorithm meets the following functional requirements in charging scheduling task: 1) CCP-compatibility, 2) sampled-data-based implementation, 3) resilience to DoS attack. Theoretical analysis demonstrates that the proposed method optimizes the charging strategy for each PEV while effectively handling the above non-ideal factors. Simulation results further confirm that under the constraints of minimizing system cost and maintaining an acceptable convergence rate, the execution count of CCP is reduced by 68% and 78% compared to the existing node-event-triggered mechanism and periodic sampling mechanism, respectively, which implies the superior communication and computational-resource efficiency of the proposed EETM framework.

This article introduces a distributed privacy-preserving economic dispatch (ED) algorithm for islanded microgrid (MG), addressing the challenge of balancing power generation and demand with minimal system cost while ensuring data privacy. The proposed algorithm utilizes homomorphic encryption to establish a confidential interaction protocol (CIP), safeguarding sensitive information during the ED process. To mitigate the computational and communication overheads of the CIP, the CIP-compatible static and dynamic event-triggered mechanisms (ETMs) are, respectively, developed, whose implementations only rely on the information obtained through the proposed CIP. The ETMs reduce the frequency of CIP execution and make the algorithm suitable for resource-constrained environments. The convergence and privacy preservation of algorithms are theoretically proved, and the simulation results validate the algorithm’s effectiveness, highlighting its advantages in privacy protection and resource efficiency.

This article proposes an online data-based inverse reinforcement learning (IRL) scheme to solve optimal control problem for nonlinear systems with control constraints, in which the unknown reward functions are recovered based on the systems’ demonstrated state and input data. To deal with control constraint, we introduce a saturation function to formulate the original constrained optimal control problem into a new unconstrained optimal control problem. Then a data-based identifier using neural network (NN) approximation technique is designed to estimate the system’s dynamics. Subsequently, we develop a data-based IRL approach to learn the unknown reward function and establish the weight tuning law of the value function and reward function using demonstrated state and input data. The proof of the uniform boundedness of the weight estimation error is presented. A simulation example is provided to verify the effectiveness of the proposed approach.

Dynamic state estimation (DSE) plays a vitally important role in modern power systems, and the reliance on the communication network often render the systems to cyber-threats. This paper investigates the secure DSE problem for the multi-generator power grids in the presence of randomly occurring cyber-attacks. To facilitate the decentralized DSE, the synchronous generator is decoupled form the large-scale interconnected power grid with the aid of model decoupling method. A hybrid cyber-attack model, which includes three typical and representative attacks (i.e., denial-of-service attacks, bias injection attacks and replay attacks), is designed and launched in a random way. Attention is devoted to the secure algorithm design problem to light the negative impacts on the DSE performance from the nonlinearity/non-Gaussianity and the random occurrences of the cyber-attacks. Specifically, i) a likelihood function modification method is established where the knowledge of the hybrid-attack model is fully considered; and ii) the associated weights of the particles are updated according to the proposed likelihood function to resist the impacts caused by the randomly occurring cyber-attacks. Finally, simulation experiments with four scenarios are implemented on the IEEE 39-bus system and the corresponding analyses show the validity of the decentralized secure DSE scheme.

As renewable energy resources increasingly integrate and dynamic user loads emerge, precise short-term forecasting across various scenarios becomes crucial for the efficient operation of modern power systems. Federated learning (FL) presents an effective solution for forecasting that safeguards privacy and security through model aggregation without sharing sensitive raw data. In the Electric Internet of Things (EIoT), the heterogeneity of clients can complicate model convergence, challenging the efficiency of the current federated averaging (FedAvg)-based methods. To address this, our analysis of regression challenges within the EIoT and the unique attributes of FL methods has led to the development of a novel algorithm: federated mutual knowledge distillation learning for regression (FedMR), aiming to deliver superior forecasting precision and efficiency at a reduced cost. FedMR utilizes the models and min-max labels from distributed clients, enabling its embedded generator to assimilate global knowledge and synthesize data in a normal distribution pattern. This feature enhances training across a spectrum of user environments. Demonstrated through testing on three open EIoT data sets, FedMR showcases high efficiency, accuracy, and adaptability across diverse client participation scenarios. Additionally, our research delves into the application of FedMR in managing device heterogeneity, a common issue in the EIoT systems. By focusing on sharing only the regression layers of clients, our proposed FedMRL(ightweight) pioneers a model-agnostic aggregation approach in short-term forecasting, effectively achieving a harmonized balance of accuracy and efficiency.

In this article, the exponential stability (ES) and fuzzy control problem is addressed for DC microgrid (DC-MG) system based on T-S fuzzy model under denial-of-service (DoS) attacks. Considering that the time scale of networked T-S fuzzy model and parallel distributed compensation (PDC) fuzzy control rules is asynchronous, the T-S fuzzy model of the DC-MG system is established. More importantly, in order to reflect the effect of DoS attacks, a time-constrained DoS attack (TCDA) model, which only characterizes the duration of DoS attacks, is established compared with the classic DoS attack model. Then, a switched DC-MG fuzzy system model based on state-feedback control law and TCDA model is established. Furthermore, the time-varying Lyapunov function related to attack parameters is used to ensure the ES of the system. Besides, a fuzzy-dependent switching control strategy is designed in terms of linear matrix inequalities (LMIs). Finally, through a simulation example, the effectiveness of the proposed control strategy is verifiedNote to Practitioners—As a typical example of cyber-physical systems, nonlinear DC-MG system is vulnerable to malicious cyber attacks (such as DoS attack). The existing DoS attack models are usually characterized by the duration and frequency of attacks. In this paper, we adopt a new approach to describe the DoS attack model using only attack duration characteristics. This makes the DoS attack model more general. We then propose a switching fuzzy control algorithm subject to intermittent DoS attacks and derive sufficient conditions for tolerable duration of attack. This can allow practitioners to know under what conditions the ES of the attacked system can still be guaranteed. The effectiveness of theoretical analysis results is verified by simulation experiments. In the future research, we will address the design of fuzzy control algorithms for DC-MG under various cyber attacks.

In this paper, the issue of finite-time fault-tolerant tracking control via event-triggered strategy is investigated for a general class of non-affine systems with a semi-bounded structure. The differentiable condition imposed on the non-affine terms is removed. A new model transformation method is used to transform the non-affine system into a pseudo-affine one, which generates an uncertain model with the undesired overflowed variables. We utilize the neural networks to approximate the uncertain functions and separate the overflowed variables to guarantee the solvability of virtual controllers in the iterative design. In addition, the tracking error is limited to a specified range within a finite time by the introduced prescribed performance. By considering the actuator fault and the limited communication resources, an event-triggered fault-tolerant control scheme is proposed, which not only ensures finite-time tracking, but also avoids the Zeno behavior and reduces the waste of communication resources. Finally, the proposed method is used for the microgrid systems.

The increasing number of devices and frequent interactions of agents from networked multiagent systems (MASs) exacerbate the risks of potential cyber attacks, especially the different point attacks and multiple pattern attacks. This article considers the output formation-containment problem for MASs under multipoint multipattern false data injection (FDI) attacks. The multipoint describes the attacks simultaneously occurring on the sensors, actuators, and communication channels; the multipattern captures that sensor and actuator attack signals are both continuous deterministic variables, and the communication channel attack signals are intermittent random variables, obeying the Bernoulli distribution. For such compromised MASs, a novel hybrid protocol is proposed, which integrates a state observer, an attack estimator, an impulsive interactor and a compensation controller. Thereinto, the state observer and the attack estimator are constructed to recover the unmeasured system states and the unknown FDI attack signals, respectively; the impulsive interactor is designed to guarantee that the neighbor's signals are transmitted only at impulsive instants, and meanwhile the channel attacks are randomly launched; using the recovered signals, the compensation controller is devised to alleviate the effect of attacks. A sufficient condition is identified, under which the output formation containment is achieved with cooperative uniform ultimate boundedness (UUB). Finally, simulation results are carried out to validate the effectiveness and advantages of the proposed approach.

To enhance system robustness in the face of uncertainty and achieve adaptive optimization of control strategies, a novel algorithm based on the unscented Kalman filter (UKF) is developed. This algorithm addresses the finite-horizon optimal tracking control problem (FHOTCP) for nonlinear discrete-time (DT) systems with uncertainty and asymmetric input constraints. An augmented system is constructed with asymmetric control constraints being considered. The augmented problem is addressed with a DT Hamilton-Jacobi-Bellman equation (DTHJBE). By analyzing convergence with regard to the cost function and control law, the UKF-based iterative adaptive dynamic programming (ADP) algorithm is proposed. This algorithm approximates the solution of the DTHJBE, ensuring that the cost function converges to its optimal value within a bounded range. To execute the UKF-based iterative ADP algorithm, the actor-estimator-critic framework is built, in which the estimator refers to system state estimation through the application of UKF. Ultimately, simulation examples are presented to show the performance of the proposed method.

This article investigates the sliding mode control (SMC) problem for a class of uncertain 2-D systems described by the Roesser models with a bounded disturbance. In order to reduce the communication usage between the controller and the actuators, it is supposed that only one actuator node can gain the access to the network at each sampling time along horizontal or vertical direction, where a proper 2-D round-robin protocol is designed to periodically regulate the access token and a set of zero-order holders (ZOHs) is employed to keep the other actuator nodes unchanged until the next renewed signal arrives. Based on a novel 2-D common sliding function, a token-dependent 2-D SMC scheme with first-order sliding mode is appropriately constructed to cope with the impacts from the periodic scheduling signal and the ZOHs. Furthermore, a novel super-twisting-like 2-D SMC scheme with second-order sliding mode is designed to improve the robustness against the bounded disturbance. By resorting to token-dependent Lyapunov-like function, sufficient conditions are obtained to guarantee the ultimate boundedness of the horizontal and vertical states as well as the 2-D common sliding function. For acquiring the optimized gain matrices, two searching algorithms are formulated to solve two optimization problems arising from finding optimized control performance. Finally, two comparative examples are exploited to demonstrate the effectiveness and the advantageous of the proposed first- and second-order 2-D SMC design schemes under round-robin scheduling mechanism.

This article deals with the distributed proportional–integral state estimation problem for nonlinear systems over sensor networks (SNs), where a number of spatially distributed sensor nodes are utilized to collect the system information. The signal transmissions among different sensor nodes are realized via their individual channels subject to energy-constrained Denial-of-Service (EC-DoS) cyber-attacks launched by the adversaries whose aim is to block the nodewise communications. Such EC-DoS attacks are characterized by a sequence of attack starting time-instants and a sequence of attack durations. Based on the measurement outputs of each node, a novel distributed fuzzy proportional–integral estimator is proposed that reflects the topological information of the SNs. The estimation error dynamics is shown to be regulated by a switching system under certain assumptions on the frequency and the duration of the EC-DoS attacks. Then, by resorting to the average dwell-time method, a unified framework is established to analyze the dynamical behaviors of the resultant estimation error system, and sufficient conditions are obtained to guarantee the stability as well as the weighted$H_{\infty}$performance of the estimation error dynamics. Finally, a numerical example is given to verify the effectiveness of the proposed estimation scheme.

In this technical correspondence, the resilient fuzzy stabilization is enhanced in the direction of elevating the feasible stabilization region as large as possible while the same alert threshold is chosen as the recent one. To do this, the switching-type fuzzy state-feedback controller is designed with a set of switch modes so that more groups of gain matrices can be introduced to enhance the degree of freedom. What is far more important is that a novel augmented time-variant matrix approach is proposed in order to collect the proprietary features of normalized fuzzy weighting functions with regard to each switch mode. Then, all the obtained augmented time-variant matrices are split into a set of positive/negative matrices, which can be elaborately assigned into different monomials of our designing conditions under the framework of homogeneous polynomials. Therefore, less conservative results of resilient fuzzy stabilization are obtained even if some higher alert thresholds are chosen for probably ensuring the establishment of the involved precondition. Finally, the superiority of our approach is validated by giving some detailed comparisons on the benchmark example.

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A "source-storage-load" coordinated master-slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply-demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source-storage-load coordinated control method based on the "ramping speed" ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source-storage-load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

Information granularity and information granules are fundamental concepts that permeate the entire area of granular computing. With this regard, the principle of justifiable granularity was proposed by Pedrycz, and subsequently a general two-phase framework of designing information granules based on Fuzzy C-means clustering was successfully developed. This design process leads to information granules that are likely to intersect each other in substantially overlapping clusters, which inevitably leads to some ambiguity and misperception as well as loss of semantic clarity of information granules. This limitation is largely due to imprecise description of boundary-overlapping data in the existing algorithms. To address this issue, the rough k -means clustering is introduced in an innovative way into Pedrycz's two-phase information granulation framework, together with the proposed local boundary fuzzy metric. To further strengthen the characteristics of support and inhibition of boundary-overlapping data, an augmented parametric version of the principle is refined. On this basis, a local boundary fuzzified rough k -means-based information granulation algorithm is developed. In this manner, the generated granules are unique and representative whilst ensuring clearer boundaries. The validity and performance of this algorithm are demonstrated through the results of comparative experiments.

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.

This paper is concerned with a resilient load frequency control (LFC) of multi-area power systems subject to unknown load disturbances and intermittent denial-of-service (DoS) attacks. The central aim is to develop a feasible and less conservative DoS-resilient LFC scheme that constrains jammer’s actions as less as possible and explores available attack information as much as possible in desired analysis and design criteria. Towards this aim, we first present a partially known DoS model that bounds merely the DoS-on durations. We then elaborate a sampled-data-based transmission paradigm which characterizes explicitly the uniform sampling instants and intermittent DoS-on and -off instants as well as the nonuniform arriving instants of the transmitted system data. Furthermore, we develop a novel attack-parameter-dependent Lyapunov functional to establish less conservative conditions for both stability analysis and controller design. It is shown that the exponential stability of the resultant closed-loop LFC system can be guaranteed, while also preserving both the desired minimum sleeping rate of DoS attacks and the prescribedH∞performance index against changing load disturbances. Finally, several case studies of a three-area power system are provided to verify the effectiveness and superiority of the derived theoretical results.

False data injection attack (FDIA) can pose a severe threat to the distribution networks (DN), and the accurate detection of FDIA plays a key role in the safe and reliable operation of the DN. In this article, an end-edge-cloud collaboration-based detection framework is proposed to detect FDIA in the DN. First, in order to effectively preserve the privacy of different stakeholders in the DN and solve the problem of data island, a federated-learning-based edge-cloud collaboration mechanism is designed according to the proposed end-edge-cloud collaboration framework to jointly train the local FDIA detection models and eventually build a comprehensive FDIA detection model. Then, considering the temporal–spatial correlation of measurement data, a local data-driven FDIA detection model is proposed based on a novel temporal–spatial graph convolutional network, which can extract temporal–spatial features of the measurement data and improve the FDIA detection performance. In general, compared with the traditional centralized FDIA detection methods, the proposed method can make full use of the computational capacity of distributed edge devices and reduce the pressure of computation on the control center. Finally, simulation results based on the modified IEEE 14-bus and IEEE 118-bus distribution systems indicate that the proposed method can effectively improve the accuracy of FDIA detection compared with other methods.

An attitude control issue is concerned for a quadrotor with external disturbances in this paper. For unknown system dynamics, predictor-based neural networks (NNs) are introduced, where prediction errors, angular velocities, are constructed, instead of tracking errors, for updating NNs' weights. This replacement reduces the occurrence of high-frequency oscillations in NNs' approximation. With this improved NNs, a predictorbased NN disturbance observer is then developed for compensation for external disturbances and NNs' approximation errors, and a normalization learning technique is employed for reduction of the number of learning parameters. A predictor-based neural attitude control strategy is proposed for a quadrotor with external disturbances. Furthermore, measurement noise are taken into account in our predictor-based neural attitude control strategy. The Lyapunov-based stability analysis shows that all closed-loop signals in the designed attitude system are semiglobally bounded. A numerical simulation and a hardware-in-loop experiment as well as outdoor flight verify the effectiveness of the proposed anti-disturbance attitude control strategy.

In this article, a multiple time-scale voltage regulation is proposed based on the three-level coordinated control approach. This approach aims at solving the voltage issues in different scopes of space-time for active distribution networks. First, to address the global voltage issue of the entire network on a relatively slow-time scale, a multimode switching control is designed based on the Petri-net, which can effectively switch the on-load tap changer to extend its service life. Second, a multiobjective optimization considering the voltage differences with the security boundary and the transmission loss of the entire network is proposed. As such the global voltage issue can be handled by cooperating with the first-level switching control. Third, to solve the local voltage issue on a fast-time scale, a fully distributed optimal control integrating the active/reactive power sharing of all the distributed units is proposed. This control facilitates the control efficiency and active power consumption for renewable energy sources. Finally, the efficiency and effectiveness of the proposed method are validated under different scenarios in case studies.

Voltage is an imperative index for the successful operation of distribution networks. With a high penetration of photovoltaic, the intermittence can cause voltage violations that become severer especially during midday and nightfall periods. Coordinated power compensation has shown promising results in rapid voltage regulation while depending on communications. This article proposes a centralized event-triggered voltage regulation method incorporating the impacts of time delay. In order to makes all bus voltages acceptable, a sequential proportional power compensation is first proposed for invariable crucial bus. Then, an event-triggered mechanism is designed by considering multivariate changes under power fluctuations, thus reducing communication burden. Using a quantitative upper boundary obtained here, the impacts of time delays on steady-state regulation performance are fully analyzed. Finally, simulation on MATLAB is conducted to indicate the effectiveness of this method.

In order to improve the voltage quality of buses in active distribution networks with high photovoltaic penetration, power compensation of controllable resources is widely used. How to develop a method to reduce communication burden while facilitating their optimal coordination is rarely discussed. This article designs an optimal voltage regulation method via hybrid power compensation. First of all, an event-triggered mechanism based on multiconstraint of changes in power flow is designed, thereby dividing all instants into trigger and nontrigger types. Both voltage offset at trigger instants and voltage fluctuation at nontrigger instants are mitigated by using a hybrid coordinated power compensation. By establishing a quantitative index, the optimal tradeoff performance for hybrid power compensation is analyzed under the event-triggered way. Finally, the case study verifies the effectiveness of the proposed method.

The integration of large-scale distributed generators into active distribution networks (ADNs) will aggravate voltage fluctuations, which can affect the secure operation of power grids seriously. In this article, we investigate a cooperated voltage regulation problem of ADNs. Specifically, we first formulate a two-timescale voltage regulation problem considering the coordination of various hybrid devices while reducing the power loss of the whole ADNs. Given that the aforementioned problem is challenging to solve directly, we reformulate it as bilevel Markov games. Then, we propose a hierarchical multi-agent attention-based deep reinforcement learning algorithm to solve them. To be specific, the upper level Markov game is solved by a discrete multi-actor-attention-critic (MAAC) algorithm, and the lower level Markov game is solved by a continuous MAAC algorithm. In addition, the two-timescale coordination between upper level and lower level agents is implemented through the information exchange of rewards during the training process. Simulation results show that the proposed algorithm has good effectiveness, robustness, and scalability in voltage regulation.

The volatility and randomness of the source-load sides increase the difficulty for system frequency regulation, while flexible loads like thermostatically controlled loads (TCLs) and hydrogen energy storage system (HESS) can be used to provide frequency auxiliary services. In this article, a hierarchical frequency control framework is established, with TCLs and HESS interacting in a distributed cooperative way to track unmatched power. Then, power allocation principles are proposed, which realizes the rational utilization of various resources and ensures the close tracking of the upper-level power targets. Further, a leader–follower distributed dynamic event-triggered control strategy is designed to guarantee fast convergence speed and synchronization accuracy, where power transmission on demand is realized to save limited network resources. Moreover, sufficient conditions are derived to ensure the stability of the distributed TCLs and HESS system by combining the Lyapunov theory. Finally, case studies assess that the proposed strategy guarantees the effectiveness and rapidity of the frequency control, meanwhile reducing the communication burden.

In this article, a learning-based resilient fault-tolerant control method is proposed for a class of uncertain nonlinear multiagent systems (MASs) to enhance the security and reliability against denial-of-service (DoS) attacks and actuator faults. With the framework of cooperative output regulation, the developed algorithm consists of designing a distributed resilient observer and a decentralized fault-tolerant controller. Specifically, by using the data-driven method, an online resilient learning algorithm is first presented to learn the unknown exosystem matrix in the presence of DoS attacks. Then, a distributed resilient observer is proposed working against DoS attacks. In addition, based on the developed observer, a decentralized adaptive fault-tolerant controller is designed to compensate for actuator faults. Moreover, the convergence of error systems is shown by using the Lyapunov stability theory. The effectiveness of our result is examined by a simulation example.

This article is concerned with the maximum correntropy filtering (MCF) problem for a class of nonlinear complex networks subject to non-Gaussian noises and uncertain dynamical bias. With aim to utilize the constrained network bandwidth and energy resources in an efficient way, a componentwise dynamic event-triggered transmission (DETT) protocol is adopted to ensure that each sensor component independently determines the time instant for transmitting data according to the individual triggering condition. The principal purpose of the addressed problem is to put forward a dynamic event-triggered recursive filtering scheme under the maximum correntropy criterion, such that the effects of the non-Gaussian noises can be attenuated. In doing so, a novel correntropy-based performance index (CBPI) is first proposed to reflect the impacts from the componentwise DETT mechanism, the system nonlinearity, and the uncertain dynamical bias. The CBPI is parameterized by deriving upper bounds on the one-step prediction error covariance and the equivalent noise covariance. Subsequently, the filter gain matrix is designed by means of maximizing the proposed CBPI. Finally, an illustrative example is provided to substantiate the feasibility and effectiveness of the developed MCF scheme.

A finite-time output consensus control problem is investigated in this article for an uncertain nonlinear high-order multiagent systems (MASs). For this class of MASs, the order of individual follower is reduced gradually by implementing the immersion and invariance (I&I) control theory repeatedly, and a requirement of solving partial differential equations (PDEs) in I&I control theory is obviated. Furthermore, an I&I-based radial basis function neural network (RBFNN) approximator is developed, where an extra cross term is added in the approximation mechanism, and the form of an update law for weights is transformed into a proportional and integral one. This I&I-based RBFNN approximator does not rely on a cancellation of the perturbation term, and these uncertainties are reconstructed by the I&I manifold adaptively, which is for improvement of approximation behaviors of traditional RBFNNs. On this basis, a distributed adaptive forwarding finite-time output consensus control strategy is proposed by combining a sign function, and the convergence time of the MAS can be adjusted with appropriate finite-time parameters. Finally, two illustrative examples verify the effectiveness of the theoretical claims.

The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology's view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

To address the false data injection (FDI) and denial of service (DoS) attack, this article proposes an event-trigger-based resilient distributed energy management approach for cyber–physical system of smart grid. Here, an event-trigger-based resilient consensus algorithm (ERCA) is proposed with the attack identification and compensation mechanism. The event-triggered mechanism is improved within distributed optimization combined with reliable acknowledgment (ACK) signals technique to mitigate the impact of data loss or transmission delay, and trust nodes-based compensation approach is proposed during resilient coordinated optimization for state correction to ensure the stability and security of power grid system. The optimality and convergence of the proposed method are proved theoretically that the proposed method can approximate to optimal solution well and achieve consensus by ensuring the proactive involvement of all participants under coordinated cyber attack. According to those obtained simulation results, it reveals that the proposed algorithm can effectively solve the energy management issue under coordinated DoS and FDI attack.

This article addresses the optimal containment problem of heterogeneous multiagent systems (MASs) with dynamic leaders via reinforcement learning (RL), where the dynamics of all agents are all completely unknown. A distributed model-free observer is constructed for each follower to estimate the leaders’ dynamics and the output trajectories inside the convex hull formed by the leaders. Based on the designed observers, the optimal containment problem is formulated as an optimal tracking control issue. Then the discounted performance functions are introduced to obtain algebraic Riccati equations (AREs). And a model-free RL algorithm is developed to learn the AREs online. To implement this algorithm, we design a single critic neural network structure for each follower to approximate Q-function, and estimate optimal control policy and worst-case adversarial input policy. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed algorithm.

The proportion of distributed resources connected to the distribution network is gradually increasing. But in most scenarios, resources operate in passive response mode and cannot give full play to their active regulation potential. To awaken the regulation capability of distributed resources for active support of system frequency stability, a transmission and decision-making co-designed architecture is studied in this paper when a local supply-demand imbalance in the distribution network causes frequency instability. The architecture contains “data module”, “transmission module”, and “decision-making module”. Firstly, in the “data module”, based on the theory of multi-extreme learning machines and power flow calculation, the generation prediction of energy sources is performed to provide the data basis for generating regulation commands. Secondly, in the “transmission module”, through the nodal current equation-based sliding mode control and fast path reconstruction, the response strategy of the communication disturbance problem is proposed to provide the transmission support for generating regulation commands. Finally, based on the theory of “multiple factors matching” and “source-load interaction”, the source and load-side regulation commands are generated in the “decision-making module” by combining the transmitted data. Related case studies are carried out to verify the effectiveness of the proposed strategies.

In the multi-microgrid system, once a microgrid is severely disturbed into the alert or emergency state, the effective multi-mode management method is necessary to make the system restore the balance of supply and demand rapidly. Therefore, a hierarchical multi-mode management strategy is proposed in this study, which includes three steps:1. The first step is to predict and fit the support capacity of the neighbor microgrids and to determine whether these microgrids need to participate in the support, which constitutes the upper layer. To this end, an event-triggered mode management strategy is proposed considering the multi-source fitting and line loss factor, etc. 2. When the support of neighbor microgrids is not required, the second step is to manage local source-storage-load to restore the balance of supply and demand rapidly, which constitutes the lower layer. To this end, another event-triggered management strategy is designed to generate management commands without pre-processing data; 3. To establish the mathematical models of a microgrid in hybrid mode consisting of continuous operation status and discrete management commands, the related small signal model is designed as the third step, which can analyze the system stability conveniently. Finally, the effectiveness of methods is verified by case studies.

Neural dynamic surface control (NDSC) is an effective technique for the tracking control of nonlinear systems. The objective of this article is to improve closed-loop transient performance and reduce the number of learning parameters for a strict-feedback nonlinear system with unknown control gains. For this purpose, a predictor-based NDSC (PNDSC) approach is presented. It introduces Nussbaum functions and predictors into the traditional NDSC for nonlinear systems with unknown control gains. Unlike NDSC that uses surface errors to update the learning parameters of neural networks (NNs), the PNDSC employs prediction errors for the same purpose, leading to improved transient performance of closed-loop control systems. To reduce the number of learning parameters, the PNDSC is further embedded with the technique of the minimal number of learning parameters (MNLPs). This avoids the problem of the "explosion of learning parameters" as the order of the system increases. A Lyapunov-based stability analysis shows that all signals are bounded in the closed-loop systems under PNDSC embedded with MNLPs. Simulations are conducted to demonstrate the effectiveness of the PNDSC approach presented in this article.

This article is concerned with developing a featured multi-instant Luenberger-like observer of discrete-time Takagi-Sugeno fuzzy systems with unmeasurable state variables, that is, not only to reduce the conservatism but also (at the same time) to alleviate the computational complexity over the recent approach reported in the literature. Contrary to previous approaches, an enhanced gain-scheduling mechanism is proposed for constructing much abundant working modes by online evaluating the updated variation information of normalized fuzzy weighting functions across two adjacent sampling instants and, thus, a different group of observer gain matrices with less conservatism is designed in order to employ the exclusive features for each working mode. Moreover, all the redundant terms containing both surplus and unknown system information are discriminated and removed in this study and, thus, the required computational complexity is reduced to a certain extent than the counterpart one. Finally, numerical examples are provided to illustrate the superiority of the developed approach.

In this study, the event-triggered problem of networked control systems (NCSs) is investigated, and a novel information transmission scheme is established. Under this scheme, the segment-weighted information (SWI) in a sliding historical window (SHW) is calculated and then sampled. Compared with the traditional direct sampling method, in this approach, the control input includes historical information in the SHW, thereby leading to less information loss due to sampling. This study also emphasizes on designing an SWI-based event-triggered mechanism (ETM) for scheduling network transmission. Different from most of the existing ETMs, the proposed SWI-based ETM leverages historical information to determine which data are necessary for the whole control system. Our approach can greatly reduce the number of unexpected triggering events of a control system with stochastic disturbances owing to the introduction of the SWI in the ETM. Moreover, Zeno phenomena are prevented thanks to periodic sampling. Sufficient conditions are derived based on the Lyapunov functional approach, and a numerical simulation example is provided to demonstrate the effectiveness of the proposed method.

In this article, a networked fault detection (FD) problem is investigated for interval type-2 T–S fuzzy systems. A novel adaptive memory-event-triggered mechanism (METM) is proposed by introducing historical information of the measured output in a prescribed sliding window. The current measured output in the traditional event-triggered mechanism is replaced by a weighting function-based historical information. As a result, the data releasing rate can be effectively reduced and maltriggering events aroused by unknown abrupt disturbance or measurement noise can be avoided as well. Meanwhile, an adaptive threshold depending on the historical information is utilized to further adjust the data releasing rate. The FD filter is designed and derived in terms of linear matrix inequalities to guarantee the$H_{\infty }$performance of fault detected systems. Finally, a hardware-in-loop simulation experiment platform is built to manifest the effectiveness of the proposed METM-based FD method.

The problem of enhancing the robust performance of nonlinear fault estimation (FE) is addressed by proposing a novel real-time gain-scheduling mechanism for discrete-time Takagi-Sugeno fuzzy systems. The real-time status of the operating point for the considered nonlinear plant is characterized by using these available normalized fuzzy weighting functions at both the current and the past instants of time. To achieve this, the developed fuzzy real-time gain-scheduling mechanism produces different switching modes by introducing key tunable parameters. Thus, a pair of exclusive FE gain matrices is designed for each switching mode on the strength of time-varying balanced matrices developed in this study, respectively. Since the implementation of more FE gain matrices can be scheduled according to the real-time status of the operating point at each sampling instant, the robust performance of nonlinear FE will be enhanced over the previous methods to a great extent. Finally, considerable numerical comparisons are implemented in order to illustrate that the proposed method is much superior to those existing ones reported in the literature.

The problem of relaxed state estimation of discrete-time Takagi-Sugeno fuzzy systems is studied by constructing a novel multi-instant gain-scheduling fuzzy observer. First, a multi-instant gain-scheduling mechanism with a single adjustable parameter is given for the first time in order to produce more reasonable switch modes over previous results reported in recent literature. Second, for every switch mode, a batch of specified observer gain matrices is determined by developing an efficient balanced matrix approach so that the updated values of adjacent normalized fuzzy weighting functions can be flexibly exploited. Since the implied information of each specific switch mode is capable of being absorbed and utilized more thoroughly by the aid of the refined higher-order balanced matrices, the conservatism can be prominently reduced at the price of consuming extra computational burden within the allowable range. Finally, two benchmark examples are provided to test and verify the progressiveness of our proposed approach.

Due to the existence of different stakeholders, it makes competitive game characteristic in hybrid energy systems (HESs). Combined with the high-dimensional complexity and output uncertainty of distributed energy resources, the optimal operation of HESs can be a more challenging problem. Here, this article proposes a potential game-based two-layered hierarchical optimization strategy to deal with this problem. With consideration of its high-dimensional complexity, a two-layered hierarchical HES model is created, consisting of an upper-level and a lower-level model. For properly solving competitive relationships among different stakeholders in the upper-level model, a multiagent system for stakeholders is created and a potential game is employed with a distributed primal-dual perturbed algorithm, and its convergence and optimality have been both proved. Moreover, an uncertainty and robustness analysis is done with coordination between lower and upper models, which deduces a feasible robust uncertainty interval in the lower-level model. For better dealing with the lower-level model, a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized to optimize the economic cost and emission issue simultaneously, producing a set of Pareto-optimal schemes. Combined with simulation results, it is proven that the proposed method can reduce computational complexity as well as properly deal with uncertainty problems for the optimal operation of HESs.

In this article, the tracking control problem is investigated for a type of linear networked systems subject to the round-Robin (RR) protocol scheduling and impulsive transmission outliers (ITOs). The communication between the controller and sensors is implemented through a shared network, on which the signal transmissions are scheduled by the RR protocol. The considered ITOs are modeled by a sequence of impulsive signals whose amplitudes (i.e., the norms of all impulsive signals) and interval lengths (i.e., the duration between all adjacent impulsive signals) are greater than two known thresholds, respectively. The occurrence moment for each ITO is first examined by using a certain outlier detection approach, and then a novel parameter-dependent tracking controller is proposed to protect the tracking performance from ITOs by removing the "harmful" signals (i.e., the transmitted signals contaminated by ITOs). Sufficient conditions are presented to ensure the exponentially ultimate boundedness of the resulted tracking error, and the controller gain matrices are subsequently designed by solving a constrained optimization problem. Finally, a simulation example is provided to demonstrate the effectiveness of our developed outlier-resistant tracking control scheme.

This article focuses on designing a novel stochastic data-based denial-of-service (DoS) attack strategy that can intercept measurements and reduce the quality of remote state estimation in interval type-2 (IT2) T–S fuzzy systems. The new proposed DoS attack strategy has two main characteristics. First different from most of the existing DoS models, it is data-based, which mainly attacks the packets playing important roles in the system performance. Therefore, compared with the existing indiscriminate DoS attack approaches, the data-based DoS attack is more intelligent and can cause larger disruptions. Second, the proposed attack strategy is random, which has better concealment than the periodic or consecutive DoS attack models. Then, by using a stochastic method, the relationship among attack effect, attack probability, and attack parameter are analyzed and shown in Theorem1. Furthermore, if the attacks are energy-constrained, the relation between the attack rate threshold and the upper bound of the attack parameter is expressed analytically, which is shown in Theorem2. Simulation results demonstrate that, compared with some existing DoS models, the proposed stochastic data-based DoS attack strategy brings about greater destructiveness to the estimation quality of the systems, which demonstrates the effectiveness of the proposed attack strategy from the attacker’s perspective.

In this article, the security-guaranteed fuzzy networked state estimation issue is investigated for a class of two-dimensional (2-D) systems with norm-bounded disturbances. Considering the structural specificity of the 2-D systems, the membership function in the Takagi–Sugeno fuzzy model is established to reflect the spatial information. Multiple sensor arrays are utilized to improve the observation diversity and overcome the measurement obstacle induced by geographical restrictions. The network-based deception attacks, occurring in a probabilistic fashion, are characterized by a set of Bernoulli distributed random variables. By resorting to the 2-D fuzzy blending and augmentation operations, the error dynamics of the$s$th 2-D fuzzy estimator is formulated and, subsequently, theglobally asymptotical stabilityof the local error dynamics is studied in virtue of Lyapunov stability theory, fuzzy theory, and stochastic analysis technique. Then, sufficient conditions are derived to ensure the so-called$(\varrho _{1},\varrho _{2},\varrho _{3},\rho _{s})$-securityof the local error dynamics. Furthermore, the estimation fusion problem of the local fuzzy estimators is discussed and the corresponding$(\varrho _{1},\varrho _{2},\varrho _{3},\rho _{s})$-securityis also guaranteed. Finally, an illustrative example is provided to demonstrate the rationality and the effectiveness of the proposed state estimation algorithm.

Cyber security of modern power systems has become increasingly significant due to their open communication architecture and expanding network connectivity exposed to malicious cyber attacks. Resilient control represents an effective means to preserve the survivability of the power system under cyber attacks. In this paper, we address the resilient load frequency control (LFC) design for multi-area power systems under a new class of time-constrained denial-of-service (DoS) attacks. First, different from the widely-explored duration- and frequency-constrained DoS attack models, we consider a general time-constrained DoS attack model where only the attack durations are confined into some bounds, which represents less a priori knowledge of an attacker’s actions. Second, instead of using a traditional yet conservative time-invariant Lyapunov function (TILF), we develop an attack-parameter-dependent time-varying Lyapunov function (TVLF) approach to enable a resilient LFC design without jeopardizing the desired closed-loop system stability and performance. Furthermore, we provide a formal stability and performance analysis condition as well as a design criterion for the desired DoS-resilient output feedback LFC controller. We also show that the minimum allowable sleeping period and the maximum allowable active period of the attacked LFC system can be explicitly disclosed. Finally, we present two simulation case studies on a two-area LFC system and a three-area LFC system to demonstrate the effectiveness of the obtained results.

The distribution network reconfiguration (DNR) can realize the voltage preventive control of the distribution network (DN) under alert state, which solves the voltage security issues and maintains the safe operation of the DN. However, since the great scale and complexity of the DN would make the traditional centralized DN reconfiguration method have a heavy computing burden, a DNR method based on cloud-edge collaborative architecture for voltage preventive control is proposed in this article, which can reduce the huge computational pressure caused by the excessive concentration of computing tasks. In order to formulate the optimal topology reconfiguration strategy according to the specifics of voltage alerts, a differential hybrid Petri-net model with event-triggered strategy is constructed based on the cloud-edge collaborative architecture to characterize the logical relations of the solution process of the reconfiguration strategy. Considering the short time scale characteristic of preventive control, corresponding to the solution process described by the constructed model, an evaluation network based on the graph convolutional neural network (GCN) is proposed for the reachability discrimination of solutions to significantly reduce the number of candidate solutions, as well as a decision network based on multilayer perceptron is proposed for the selection of the optimal solution among the reachable solutions. Numerical tests are conducted on the modified IEEE 33-bus and IEEE 118-bus distribution systems to validate the effectiveness of the proposed method in dealing with voltage alert problems.

In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP tasks, demonstrating its advantages in modeling long-range dependency. Recently, Transformer has also attracted tremendous attention from computer vision researchers who reformulate the image processing tasks as a sequence-to-sequence prediction but resulted in deteriorating local feature details. In this work, we propose a lightweight real-time semantic segmentation network called LETNet. LETNet combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies. Meanwhile, the elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module cultivate a positive impact on training from scratch simultaneously. Extensive experiments performed on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance. Specifically, It only contains 0.95M parameters and 13.6G FLOPs but yields 72.8% mIoU at 120 FPS on the Cityscapes test set and 70.5% mIoU at 250 FPS on the CamVid test dataset using a single RTX 3090 GPU. Source code will be available athttps://github.com/IVIPLab/LETNet.

Representation learning steered robust face image super-resolution (FSR) methods have attracted extensive attention in the past few decades. Most previous methods were devoted to exploiting the local position patches in the training set for FSR. However, they usually overlooked the sufficient usage of the contextual information around the testing patches, which are useful for stable representation learning. In this article, we attempt to utilize the context-patch around the testing patch and propose a method named context-patch representation learning with adaptive neighbor embedding (CRL-ANE) for FSR. On one hand, we simultaneously use the testing position patch and its adjacent ones for stable representation weight learning. This contextual information can compensate for recovering missing details in the target patch. On the other hand, for each input patch set, due to its inherent facial structural properties, we design an adaptive neighbor embedding strategy to elaborately and adaptively choose primary candidates for more accurate reconstruction. These two improvements enable the proposed method to achieve better SR performance than some of the other methods. Qualitative and quantitative experiments on some benchmarks have validated the superiority of the proposed method over some state-of-the-art methods.

In this article, a delay-range-dependent approach is put forward to tackle the state estimation problem for delayed impulsive neural networks. A new type of nonlinear function, which is more general than the normal sigmoid function and functions constrained by the Lipschitz condition, is adopted as the neuron activation function. To effectively alleviate data collisions and save energy, the round-robin protocol is utilized to mitigate the occurrence of unnecessary network congestion in communication channels from sensors to the estimator. With the aid of the Lyapunov stability theory, a state observer is constructed such that the estimation error dynamics are asymptotically stable. The observer existence is ensured by resorting to a set of delay-range-dependent criteria which is dependent on both the impulsive time instant and a coefficient matrix. In addition, the synthesis of the observer is discussed by using linear matrix inequalities. Simulations are provided to illustrate the reasonability of our delay-range-dependent estimation approach.

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.

Reliable data storage is crucial to the production, transmission, transaction, consumption, and analysis of an Energy Internet (EI). Whereas mainstream distributed data storage seems to be a plausible solution, the existing methods suffer from a tradeoff between the storage overhead (incurred by the replicas of data encodings for lossless recovery) and the communication latency (due to the spiking network traffic resulting from massive queries of data replicas across devices). To balance this tradeoff, we propose aLightweight Dynamic Storage Algorithm based on Adaptive Encoding(LDSA-AE) approach for EI data storage. Our key idea is to classify the data into active and inactive categories, where the active data are most likely to be accessed and thus corrupted in high frequencies. As such, wherever the active data are housed, the replicas of them can be proactively allocated into a set of nearby devices. The main challenges are to realize the classification in real-time and to tailor encoding methods for the active and inactive separately in correspondence to their own characteristics. To overcome these, our LDSA-AE 1) proposes a novel density-based clustering algorithm to tackle performance data classification in an online and unsupervised fashion and 2) leverages Minimum Density RAID-6 (MDR) code and Cauchy Reed-Solomon (CRS) code for active and inactive data encodings, respectively, striving to ensure data storage with low overhead, low latency, high reliability, and high throughput at once. A theoretical analysis substantiates the viability and effectiveness of our proposed LDSA-AE approach. We also prototype our LDSA-AE on a real-world server testbed, and the empirical study suggests the superiority of our approach over the state-of-the-art distributed storage schemes for EI in terms of storage overhead, repair throughput, and reliability.

This article mainly focuses on the problem of resilient$H_{\infty }$filtering for cyber–physical systems (CPSs) subject to denial-of-service (DoS) attacks and sensor saturation. A new event-triggered mechanism (ETM) considering both DoS attacks and limited network bandwidth is put forward to guarantee the secure performance of the filter. Under this mechanism, inherent periodical transmission attempts are generated during DoS active periods, by which the latest measurement output of the system can be successfully transmitted to the filter after the end of the DoS attack; while during DoS sleep periods, the ETM degenerates into a traditional one. Furthermore, a valid DoS attack model is proposed to further characterize the following two scenarios occurring between adjacent sampling instants: 1) the end of the DoS attack and 2) both the start of the DoS attack and the end of the DoS attack. Sufficient conditions for designing the secure filters of CPSs against DoS attacks are achieved by using the piecewise Lyapunov–Krasovskii functional approach. Finally, the validity of our designed approach is manifested by an illustration of quarter-vehicle suspension systems (SSs).

This article considers the distributed resilient cooperative control for photovoltaic generators (PVs) suffered from two types of denial-of-service (DoS) attack, where the global DoS attack jams all the communication channels and the distributed one jams each of the channels independently. The proposed control method realizes the fair utilization of all PVs, and restore the active power flow across certain transmission line and the voltage of the critical bus to their reference values. The self-triggered mechanism is introduced to ensure the control performance under the DoS attack and reduce the data flow in the communication network simultaneously. In addition, the parameter selection guidance for the resilient control method with fluctuation range, triggering frequency, and stabilization time is provided. The effectiveness of the theoretical result is verified by simulation.

In time series forecasting with outliers and random noise, parameter estimation in a neural network via minimizing the$l_{2}$loss is unreliable. Therefore, an adaptive rescaled lncosh loss function is proposed in this article to handle time series modeling with outliers and random noise. It overcomes the limitation of the single distribution of traditional loss functions and can switch among$l_{1}$,$l_{2}$, and the Huber losses. A tuning parameter in the loss function is estimated by using a “working” likelihood approach according to estimated residuals. From the proposed loss function, a robust adaptive rescaled lncosh neural network (RARLNN) regression model is developed for highly accurate predictions. In the training phase of the model, an iterative learning procedure is presented to estimate the tuning parameter and train the neural network in iterations. A new prediction interval construction method is also developed based on quantile theory. The proposed RARLNN model is applied to two groups of wind speed forecasting tasks. The results show that the proposed RARLNN model is more conducive to enhancing forecasting accuracy and stability from the perspectives of noise distribution and outliers.

This article aims to a predefined tracking precision consensus control issue for nonlinear uncertain multiagent systems (MASs) with disturbance and input saturation. Unlike the existing results of prespecified accuracy for MASs, the phenomenon of unknown control gains is solved in this article. A saturation model based on the Gaussian error function is applied due to the appearance of input saturation. The unknown disturbance is considered which can be solved by a disturbance observer. Also, to handle the problem of an unknown coefficient for the controller, the Nussbaum function is employed. Moreover, the radial basis function neural networks (RBF NNs) are utilized to estimate unknown nonlinear functions. On account of the Lyapunov stability method and backstepping technique, adaptive laws are created and the desired distributed controller is designed which guarantees that the consensus errors can converge to prescribed values. Finally, several simulation examples demonstrate the valid of the proposed method.

Due to the uncertain output of intermittent energy resources and dynamic communication topology, it brings a great challenge for the optimal security control of isolated power system. To address this problem, this article proposes a two-level optimal control strategy with event-triggered switching mechanisms. For ensuring the security of the isolated power system, event-triggered switching mechanisms are proposed in the upper-level model to decrease potential risk of supply security and voltage stability, which can ensure system security as well as a low switching cost. In the lower-level model, a distributed optimization with switching topology is developed to minimize power generation cost under the above switching mechanisms, and the convergence ability of the proposed distributed optimization method is well proved with a uniformly globally exponentially stable condition. The obtained simulation results reveal that the proposed optimization approach can properly deal with the security issue of an isolated power system as well as dynamically minimize the economic cost.

The problem of robust nonlinear fault estimation for discrete-time Takagi-Sugeno fuzzy systems is investigated by proposing a depth partitioning approach. Compared with those existing results, the working space spanned by the updated normalized fuzzy weighting functions can be partitioned into any number of non-overlapping subspaces in a systematic way and thus a new gain-scheduling fault estimation observer is developed with proprietary gain matrices concerning the current operating mode. More importantly, the conservativeness will become less and less if the prescribed number of non-overlapping subspaces varies from small to large. In other words, it provides a chance to achieve much better performance of fault estimation while a smaller degree of the homogenous matrix polynomial can be chosen, which is very important for practical applications of theoretical results. Finally, the given depth partitioning approach is applied to deal with the fault estimation of the tunnel diode circuit and its advantages are validated via simulation comparisons.

Nowadays, with the development of communication technology and its application in islanded microgrid, the pure power grid has gradually become a kind of cyber-physical system. In this system, the communication data and consensus algorithm are widely used in the secondary control of energy resources. However, in the communication process, there are also risks of network attacks, such as denial of service attack. To deal with this kind of attack existing in the data uploading channel during secondary control, the evolutionary game-based defense mechanism is designed, and the main works are as follows: firstly, the caused influence by attack on the control effect is analyzed through constructing a small-signal model. Secondly, two defense strategies including “Adjacent prediction” and “Path reconstruction” are designed. Facing different attack situations, the most suitable strategy can be selected via the evolutionary game. Thirdly, a game-based active defense strategy is designed, which can simulate the attack probability in the near future so that the defense can be prepared in advance. Finally, based on Laplace transformation and$\text{H}_{\infty } $robust theories, the parameter design in defense strategies is completed. The effectiveness of the above strategies is shown in multiple case studies.

To improve the voltage and power sharing of distributed generations, the hierarchical controls are widely used in microgrids while the dependent communication network makes it difficult to ensure the performance under limited bandwidth. In this paper, a predictive voltage hierarchical controller is designed. With the delayed secondary PI compensation signals, an inner-loop robust control is designed in the primary controller, ensuring stable voltage tracking. Then, a predictive controller is designed to provide neighbor and local predictions, such that the data integrity is improved by an accurate predictive compensation under bidirectional data-loss. Final case study results verify the effectiveness of the proposed method.

Due to the high resistance/reactance (R/X) ratio of a low-voltage microgrid (LVMG), virtual complex impedance-based P-· V/Q-ω droop control is adopted in this article as the primary control (PC) technique for stabilizing the system. A distributed event-triggered restoration mechanism (ETSM) is proposed as the secondary control (SC) technique to restore the output-voltage frequency and improve power sharing accuracy. The proposed ETSM ensures that neighboring communication happens only at some discrete instants when a predefined event-triggering condition (ETC) is fulfilled. In general, the design of the ETC is the crucial challenge of an event-triggered mechanism (ETM). Thus, in this article, a static ETM (SETM) is proposed as the ETC at first, where two static parameters are utilized to reduce the triggering frequency. Bounded stability is ensured under the SETM, which means that the output-voltage frequency is restored to the vicinity of its nominal value, and close to fair utilization of the distributed generators (DGs) is achieved. To further improve the power sharing accuracy and accelerate the regulation process, a dynamic ETM (DETM) is then introduced. In the DETM, two dynamic parameters that converge to zero in the steady state are designed, which promises asymptotic stability of the system. Besides, Zeno behavior is excluded in both mechanisms. An LVMG consisting of four DGs is constructed in MATLAB/Simulink to illustrate the effectiveness of the proposed methods, and the simulations correspond with our theoretical analysis.

filtering for Takagi-Sugeno fuzzy-model-based nonlinear networked systems with multisensors. A weighted fusion approach is adopted before information from multisensors is transmitted over the network. A novel event-triggered mechanism is proposed, which allows us not only to reduce the data-releasing rate but also to prevent abnormal data being potentially transmitted over the network due to sensor measurement or other practical factors. The problem of denial-of-service (DoS) attacks, which often occurs in a communication network, is also considered, where the DoS attack model is based on an assumption that the periodic attack includes active periods and sleeping periods. By employing the idea of the switching model for filtering error systems to deal with DoS attacks, sufficient conditions are derived to guarantee that the filtering error system is exponentially stable. Simulation results are given to demonstrate the effectiveness of the theoretical analysis and design method.

In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level.

This article studies the attack-resilient output formation containment of general high-order heterogeneous multigroup systems under unknown unbounded attacks. The multigroup systems consist of cooperative heterogeneous leaders and followers, as well as adversarial attackers. Potential attacks on the multigroup systems consist of unknown unbounded signals generated from the attackers and injected distributedly into the actuator, the local state feedback, and communication channels of each agent to destabilize the synchronization dynamics. In contrast to the existing literature dealing with bounded disturbances, noises, and faults, which are caused unintentionally, this article studies the unknown unbounded attacks that are intentionally designed to jeopardize the system. The control objective is to make each follower's output trajectory reach the uniformly ultimately bounded (UUB) convergence to the time-varying formation reference, that is, the centroid of the multiple leaders' output trajectories while keeping a predefined time-varying offset with respect to it. Fully distributed attack-resilient control protocols are proposed, without requiring any global information. Lyapunov techniques are used to analyze the stability and UUB synchronization result of the overall closed-loop system. Comparative simulation examples are given to validate the proposed results.

This article investigates the problem of resilient secure control of cloud-aided 5-DOF active semivehicle suspension systems (SVSSs). A novel joint model considering both the event-triggered mechanism (ETM) and Denial of Service (DoS) attacks is established. Under such a model, during the active period of DoS attacks, periodic transmission attempt is made to ensure the control system can receive the control information at the earliest time; the ETM turns to be a conventional one when the DoS attack is in sleeping period. Meanwhile, the valid attack period is proposed to address the problem of the DoS attack ending within a sampling period, which is a challenging problem in modeling DoS attacks. Takagi–Sugeno (T–S) fuzzy model is used to characterize the uncertainties of sprung and unsprung mass of SVSSs. By converting the cloud-aided active SVSS into a fuzzy-based switched time-delay system, and using the method of piecewise Lyapunov function, sufficient conditions are derived to ensure the performances of active SVSSs subject to DoS attacks. Finally, the effectiveness of the proposed method is validated by a numerical example of active SVSSs subject to DoS attacks.

This article is concerned with attack-resilient event-triggered$H_{\infty }$filtering for a class of networked nonlinear systems described by an interval type-2 (IT2) fuzzy model. Suppose that data transmission from the plant to the filter is completed through a wireless sensor network subject to denial-of-service attacks (DoS). In order to save the limited network bandwidth and resist the effects of DoS attacks, a resilient event-triggered communication scheme is devised. Then, an attack-resilient IT2 filter model is introduced to estimate system states of the nonlinear plant. Based on a piecewise Lyapunov–Krasovskii functional, sufficient conditions are obtained to ensure that the filtering error system is exponentially stable and satisfies a certain$H_{\infty }$performance level. Moreover, explicit expressions for the attack-resilient filter gain parameters and event-triggering parameters can be derived if a set of linear matrix inequalities are feasible. Finally, a practical example is provided to demonstrate the effectiveness of the proposed theoretical results.

This study is concerned with proposing much more effective Luenberger-like fuzzy observers than relevant multiinstant observers reported in recent literature, in other words, less conservative results can be given while less computing resource is consumed as an important premise. For the first time, the variation bound of two adjacent time-variant Lyapunov matrices encountered in the synthesis of fuzzy observer is characterized by a contraction factor, and thus the key replacement of the lagged term is developed in order to construct an effective synchronization framework of fuzzy observer synthesis. Since there only exist the current-time normalized fuzzy weighting functions in the obtained synchronization framework, the conservatism caused by the mismatch of different sampling instants can be avoided. Based on this, a lightweight gain-scheduling law is designed in which a large number of redundancy gains in recent multiinstant observers can be condensed in order to save computing resource to the greatest extent. Finally, two benchmark examples are borrowed for validating the superiority of our proposed results.

In this article, an event-triggered practical fixed-time containment control issue for stochastic nonlinear multiagent systems is addressed. The event-triggering mechanism is designed for the controller update which is reduced update frequency. In combination with the backstepping technique and fuzzy logic systems, an adaptive fuzzy containment control strategy is developed for stochastic uncertain multiagent systems. By adopting the protocol, an adaptive containment controller is devised, which can ensure that the containment errors gather to a small range in fixed time. Finally, simulation results for the practical example demonstrate the correctness of the proposed scheme.

Secure frequency control of multi-area hybrid power systems with wind power is a research problem involving active defense, vulnerability, and resilience. Considering the scenario that Denial-of-Service (DoS) attack intrude into the control channels of thermal power and wind farm, the hybrid power system is modeled by a switched system with four subsystems. Then, the exponential stability of hybrid power system is studied under arbitrary DoS attack. An active defense scheme is proposed to design switched control gains by Lie algebra method achieved by a distributed consensus method. Furthermore, following the resulted exponential stability, the load disturbance attenuant performance of frequency control is studied by the proposed concepts of vulnerability point and resilience point. Under a class of event-DoS attack model, the estimation method of vulnerability point and resilience point is given. Finally, simulations of a three-area hybrid power system and NE39bus test system are carried out to verify our theories.

In this article, a source–grid–load-collabora tion-based control framework is proposed to improve the power quality of active distribution networks (ADNs) with high penetration of low-carbon energy. First, hybrid dynamics of ADNs are characterized by addressing the voltage regulation and operation economics in each operation mode, and the mode switching control is designed in line with the operation principle of the on-load tap changer, where voltage security events are used to build the event-triggered functions. Second, multiobjective optimization is formulated with consideration of the system-wide operation cost and distribution circuit loss of the ADN in a relatively slow time scale, while in the fast time scale, all the inverter-based distributed generators, energy storages, and static var compensator devices are coordinated at the source–load side, through which multiple voltage issues, including voltage profile issue and voltage increment issue, can be addressed in a fully distributed manner. Finally, simulation results validate the effectiveness and robustness of the proposed method based on the modified IEEE 33-bus system.

In this article, a cloud-edge collaboration based control framework is proposed for the voltage regulation and economic operation in incremental distribution networks (IDN). The voltage regulation and economic operation, usually considered in separated aspects, can be integrated in a hierarchical control method by coordinating the active power and reactive power of distributed generators (DGs) and distributed storages (DSs) in an “active” mode. Promising the voltage security of the IDN, the upper level multiobjective optimization is formulated to maximize the consumption of the DGs, moreover, the lower level model predictive control (MPC) aims to regulate the dynamics of the DGs and DSs based on the established state space model. Time delay in the downstream channel is considered due to the open environment of the proposed control framework, which can be eliminated by using the PCM derived from the MPC considering model uncertainty. Finally, simulation results demonstrate the validity and robustness of the proposed method.

In recent years, how to strike a good trade-off between accuracy, inference speed, and model size has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to alleviate these problems. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining the high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multi-scale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration.

This article is concerned with bipartite tracking for a class of nonlinear multiagent systems under a signed directed graph, where the followers are with unknown virtual control gains. In the predictor-based neural dynamic surface control (NDSC) framework, a bipartite tracking control strategy is proposed by the introduction of predictors and the minimal number of learning parameters (MNLPs) technology along with the graph theory. Different from the traditional NDSC, the predictor-based NDSC utilizes prediction errors to update the neural network for improving system transient performance. The MNLPs technology is employed to avoid the problem of "explosion of learning parameters". It is proved that all closed-loop signals steered by the proposed control strategy are bounded, and the system achieves bipartite consensus. Simulation results verify the efficiency and effectiveness of the strategy.

This article investigates the problem of event-triggered switched fault-detection filters for networked stochastic systems under Denial-of-Service (DoS) jamming attacks. The considered DoS attacks are imposed by a power-constrained pulse-width modulated (PWM) jammers. A new resilient event-triggered communication strategy is designed to save network resources while counteracting the periodic PWM DoS jamming attacks. A new event-based switched residual model for fault detection is established, which characterizes the effects of the event-triggering scheme and DoS attacks simultaneously. By employing a piecewise stochastic Lyapunov functional method, sufficient conditions for achieving the exponentially mean-square stability and the prescribed performance of the residual system under the DoS attacks are formulated in terms of linear matrix inequalities. Consequently, co-design of the desired fault-detection filter parameters and the triggering parameters is achieved if the previous presented conditions are feasible. At last, an F-18 aircraft model is provided to show the validity of the proposed theoretical results.

Resilient distributed coordination control is studied on multiarea power systems with low inertia under hybrid attacks, including denial-of-service (DoS) attack and deception attack. The communication among various areas under the DoS attack is deteriorated to switching residual topologies whose time characteristic is modeled by model-dependent average dwell time (MDADT). Deception attack with malicious strategy targeting at negative feedback control is modeled by a sign function. To obtain resilience performance of the power system under low inertia and hybrid attacks, resilient distributed scheme combining load-frequency control (LFC) with virtual inertia control (VIC) is proposed. Then, resilient frequency control problem of the studied power system is converted to$H_{\infty }$control of the switched nonlinear system. By employing the Lyapunov stability theory and switched system method, the resilient conditions are given by the lower bound of the average dwell time of each residual topology and the upper bound of deception attacks. Furthermore, a linear matrix inequality (LMI) technique is used to design the distributed resilient control gains of the LFC-VIC scheme. Finally, a simulation of four-area power systems is carried out to verify the validness of our theory.

This article considers the problem of adaptive prescribed performance tracking control via slow switching for a general class of uncertain switched nonlinear systems (SNSs) with unmodeled dynamics (UDs) and nonstrict-feedback structure. The UDs are not in their form of the input-to-state practical stability and their state information is unmeasurable. By reassigning the function variables, the coupling effects between UDs and the prescribed performance function are eliminated through the iterative process. In virtue of the neural networks (NNs) approximation capability, a novel adaptive backstepping procedure is proposed without adding extra first-order filters. By choosing an appropriate slow switching law, all the signals of the closed-loop system are bounded, and the system output tracks the reference signal with a prescribed performance level (PPL).

In this article, we investigate a novel reinforcement-learning (RL)-based scheme to address the optimal leader–follower consensus issue for constrained-input continuous-time multiagent systems. First, as for input-constrained problems, the fundamental smooth assumption is not satisfied for the value function. To deal with this problem, we employ the method of vanishing viscosity solutions to relax this smooth assumption to a continuity assumption for the value function, which broadens the scope of RL applications. Second, the control cost functions take a more general form which guarantees the continuity of the optimal control policy instead of the specific integrand form used in previous input-constrained RL-based schemes. Based on these results, we introduce a novel identifier–critic–actor structure to extend the conventional critic–actor RL framework into a distributed model-free one, where the learning of the identifier, critic, and actor is online and simultaneous. We provide the simulation examples to validate the effectiveness of the proposed scheme.

Relaxed fault estimation (FE) criteria of a class of discrete-time nonlinear plants are proposed based on a new multi-instant real-time scheduling fuzzy FE observer. Compared with the existing methods given in the recent literature, the newly developed fuzzy observer is capable of providing much freedom for reducing the conservatism of designing feasible FE criteria. At each sampling point, the information of adjacent groups of normalized fuzzy weighting functions can be updated and utilized for deciding the activated mode of our developed fuzzy FE observer. Based on this, an exclusive pair of gain matrices can be given by introducing a set of well-designed free matrices into the designed FE criteria for every activated mode. Moreover, it can be found that the proposed result will include the previous one as a special case if all of those free matrices are removed. Finally, the well-known nonlinear truck–trailer plant with actuator fault is utilized to validate the superiority of our approach over the existing ones reported in the literature.

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.

A novel compensation control method for heterogeneous multiagent systems under Denial-of-Service (DoS) attacks and transmission delays is investigated in this article. This control method has all the advantages of the cloud-based computation strategy, the adaptive event-triggered strategy, and the predictive control scheme. The adaptive event-triggering mechanism can adjust the event numbers adaptively, the predictive control can reduce or eliminate the negative effects brought out by both DoS attacks and transmission delays actively, while the cloud-based computation strategy can eliminate the negative effects completely as the same as there are no DoS attacks and transmission delays. Through the interval decomposition skill and the augmented system modeling method, the compensated geschlossenes system model is established. Moreover, the joint design for the feedback gain matrices and the event-triggered parameters is implemented. In the simulation part, five VTOL aircraft are used to demonstrate the theoretical results.

Learning an expressive representation from multi-view data is a key step in various real-world applications. In this paper, we propose a semi-supervised multi-view deep discriminant representation learning (SMDDRL) approach. Unlike existing joint or alignment multi-view representation learning methods that cannot simultaneously utilize the consensus and complementary properties of multi-view data to learn inter-view shared and intra-view specific representations, SMDDRL comprehensively exploits the consensus and complementary properties as well as learns both shared and specific representations by employing the shared and specific representation learning network. Unlike existing shared and specific multi-view representation learning methods that ignore the redundancy problem in representation learning, SMDDRL incorporates the orthogonality and adversarial similarity constraints to reduce the redundancy of learned representations. Moreover, to exploit the information contained in unlabeled data, we design a semi-supervised learning framework by combining deep metric learning and density clustering. Experimental results on three typical multi-view learning tasks, i.e., webpage classification, image classification, and document classification demonstrate the effectiveness of the proposed approach.

Formation tracking is a critical issue in the consensus control of multiagent systems (MASs). This article presents a time-varying formation tracking strategy with predefined performance for a class of uncertain nonaffine nonlinear MASs connected through a directed topology. The nonaffine nonlinear MASs are transformed into affine nonlinear ones with uncertainties via the idea of active disturbance rejection control (ADRC). The uncertainties in the MASs are approximated and compensated by extended state observers (ESOs) in real time. Tracking differentiators (TDs) are introduced to reduce the complexity in the computation of the derivatives of virtual control variables. Employing funnel variables, our strategy guarantees the formation of tracking errors to stay within the desired ranges, thus improving the control performance of the closed-loop system. It is proved that all signals of the system are bounded and the formation errors can be made arbitrarily small within a residue around the origin by appropriate choices of control parameters. Case studies are carried out to demonstrate the effectiveness of the proposed control strategy.Note to Practitioners—The motivation of this article is to present a time-varying formation tracking strategy with predefined performance for a class of uncertain nonaffine nonlinear MASs within a directed topology. To simplify the process of solving the formation tracking problem, the presented strategy incorporates ADRC with the backstepping technique. Employing ADRC, our strategy approximates the uncertainties of the MAS followers via ESOs. The uncertainties are then compensated through real-time estimations of extended states. Moreover, with ADRC, TDs are used to estimate the derivatives of complex nonlinear functions, eliminating the requirement of the operations of higher order derivatives of virtual control variables. It provides a feasible strategy for industrial applications.

Multi-functional grid-tied inverters (MFGTIs) have been investigated recently for improving the power quality (PQ) of microgrids (MGs) by exploiting the residual capacity (RC) of distributed generators. Several centralized and decentralized methods have been proposed to coordinate the MFGTIs. However, with the increasing number of the MFGTIs, it demands a method with improved reliability and flexibility, which are characteristics of distributed framework that has not been introduced into the PQ improvement (PQI) field before. In this paper, we propose a distributed consensus method to undertake the PQI task. The task is proportionally shared among the MFGTIs according to their instant RCs. Besides, most of the existing methods assume that the RCs of the MFGTIs are sufficient for tackling the PQ problem (PQP), which is not always true. In the case of insufficient RC, the active power output of each MFGTI is scaled down by the same factor determined by a proposed leader-follower protocol to make room for the task. In summary, the PQP is dealt with in both cases of sufficient and insufficient RC under the distributed control framework. Finally, simulations and hardware-in-the-loop experiments of an MG consisting of three 10kVA MFGTIs are presented to verify the effectiveness of the proposed methods.

The relative state between neighbors represents the difference of two connected agents' states, and it possesses specific physical meanings in practice. Under this background, the saturation constraints in the relative state inevitably occur. This article studies the consensus problems under the relative state saturation constraints. Novel adaptive proportional-integral (PI) protocols are designed to solve the constrained consensus problem. Specifically, the adaptive coupling weights and the saturation functions are embedded into the proposed protocols, and the former can render the protocols independent of any global topology graph information, while the latter can confine the relative state to stay in its constrained set. Sufficient conditions are identified under which the constrained consensus can be achieved. Considering that the solution matrix is required to be diagonally dominant, an iterative learning-based heuristic algorithm is proposed to seek the diagonally dominant positive-definite solution matrix. For the special case that the input matrix is row full rank, more stringent saturation functions are constructed, and it not only achieves the constrained consensus but also realizes the nonovershoot and shorter settling time associated with edge states. Besides, this result can be applied to preserve connectivity of the communication network. The theoretical analyses are validated by a simulation example.

This article is concerned with the problem of observer-based dynamic event-triggered control for a networked control system (NCS) under a class of power-constrained denial-of-service (DoS) attacks that aim at impeding the network communication from time to time. First, by carefully modeling such DoS attacks as aperiodic pulse-width-modulated (PWM) jamming signals, a switching observer, adapting to the DoS attacks, is delicately constructed to deal with the unavailability of full-state information. Second, to economize the limited bandwidth resources, a dynamic event-triggered communication scheme is designed under the aperiodic DoS jamming attacks, whose duration and frequency are assumed to be restricted. Third, a switching system model with artificial state delay is formulated, which characterizes the effects of the aperiodic DoS attacks and event-triggered communication scheme in a unified framework. Then, the asymptotic stability analysis and controller/observer synthesis conditions of the resulting switching system are obtained by using a piecewise Lyapunov-Krasovskii functional approach. Furthermore, a co-design method of the dynamic triggering parameters, controller, and observer gains is presented. Finally, an example is employed to verify the effectiveness of the obtained results.

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.

Consensus control of multiagent systems (MASs) has applications in various domains. As MASs work in networked environments, their security control becomes critically desirable in response to various cyberattacks, such as denial of service (DoS). Efforts have been made in the development of both time- and event-triggered consensus control of MASs. However, there is a lack of precise calculation of control input during the attacking periods. To address this issue, a distributed secure consensus control with event triggering is developed for linear leader-following MASs under DoS attacks. It is designed with a dual-terminal event-triggered mechanism, which schedules information transmission through two triggered functions for each follower: one on the measurement channel (sensor-to-controller) and the other on the control channel (controller-to-actuator). To deal with DoS attacks, the combined states in the triggered functions are replaced by their estimations from an observer. Sufficient conditions are established for the duration and frequency of DoS attacks. To remove continuous monitoring of the measurement errors, a self-triggered secure control scheme is further developed, which combines the system states and other information at past triggered instants. Theoretical analysis shows that the followers in MASs under DoS attacks are able to track the leader and meanwhile the Zeno behavior is excluded. Case studies are conducted to demonstrate the effectiveness of our distributed secure consensus control of MASs.

In this article, a cyber-physical cooperative control strategy is proposed for islanded microgrid (MG), which divides the MG into cyber and physical layers. And the main designs in these two layers are two event-triggered mechanisms, where one mechanism is used to improve the voltage and frequency stability of MG considering the packet loss problem, the other is used to reduce the communication burden in the control process. More specifically, the control process of the first mechanism can be understood as we use these event-triggered mechanisms to complete the secondary control in the physical layer based on the information in the cyber layer. In this mechanism, the packet loss situation in one communication channel is divided into three categories: 1) to handle the case where the loss rate is small, an adaptive virtual leader-following consensus controller (AVLFCC) is proposed in the cyber layer; 2) to handle the case where the loss rate is large and the forecasted data can be used, a hybrid forecast supplement method (HFSM) is proposed in the physical layer; and 3) to handle the case where the loss rate is large and the forecasted data cannot be used, a path reconstruction method combined with a novel sliding-mode control (SMC) is proposed in the cyber layer. In the second mechanism, an event-triggered protocol is designed for the consensus controller to reduce the communication burden based on the designs in 1)-3). Finally, based on these designs in the two mechanisms, a novel secondary controller is designed. And the experimental results have confirmed the validity of the contributed strategy.

Due to uncertainty and dynamic characteristics from intermittent energy and load demand response (DR), the optimal operation of the hybrid energy system is a great challenge. This article proposes an event-triggered multiagent coordinated optimization strategy with two-layered architecture. First, the price-bidding-based DR model is proposed with different stakeholders, and it also deduces the optimal bidding price with the Nash equilibrium theory. Then, four agents are designed to control different kinds of energy resources: agent 1 mainly analyzes the uncertainty or randomness caused by intermittent power, agent 2 takes charge of the dynamic economic dispatch (DED) within thermal units, agent 3 manages the optimal scheduling of energy storage, and agent 4 mainly undertakes the load-shifting strategy from consumers. In the upper-layer level, all agents coordinate together to ensure the stability of the hybrid energy system with an event-triggered mechanism, and the intelligent control approach mainly depends on switching ON/OFF power generators or curtailing system load, and the consensus algorithm is utilized to optimize the subsystem problem in the lower-layer level. Furthermore, the simulation results can further verify the efficiency of the proposed method, and it also reveals that the event-triggered multiagent optimization strategy can be a promising way to solve the hybrid energy system problem.

This article investigates the problem of resilient control for the Takagi–Sugeno (T–S) fuzzy systems against bounded cyberattack. A novel memory-based event triggering mechanism (ETM) is developed, by which the past information of the physical process through the window function is utilized. Using such an ETM cannot only lead to a lower data-releasing rate but also reduce the occurrence of wrong triggering event. Furthermore, the frequency of event generation is relatively smoother than existing ETMs. From the current releasing instant to the next, two periods are designed. The ETM works only when the first period ends, thereby avoiding the Zeno behavior that commonly exists in continuous ETM designs. The control system is then formulated as a switched fuzzy control system with two modes in each releasing period. Based on an assumption of secure control, and the proposed ETM, sufficient conditions are obtained to guarantee the exponential stability of networked T–S fuzzy systems in the presence of deception attacks in secure sense. Finally, a single-link rigid robot is taken as an example to illustrate the advantages of theoretical results.

This article proposes an enhanced switching stabilization of Takagi-Sugeno fuzzy systems by developing a new fuzzy switching controller. A distinctive feature of the proposed approach is the enhanced ability to utilize the updated information of the normalized fuzzy weighting functions at each sampling point. Compared with those recent results in the literature, two key performance indexes, i.e., reducing the conservatism and alleviating the online computational burden, can be improved together while the off-line computational burden increases as a tradeoff but is still affordable from the point of algorithmic complexity. As a result, the efficiency of fuzzy stabilization is enhanced and thus its scope of application can be enlarged much more than before. Finally, the superiority of the proposed approach is illustrated by some simulation comparisons.

Due to the high R/X ratio and mismatched feeder impedance of low-voltage microgrids, conventional droop method is no longer able to decouple the active and reactive power of distributed generators and the powersharing accuracy is degraded. In this article, a virtual complex impedance based P - V̇ droop method is proposed to decouple the powers and improve the power-sharing accuracy among DGs. With the virtual impedance method, the equivalent impedance between virtual power source and point of common coupling is shaped to be purely resistive. Then, a P - V̇ strategy is adopted to alleviate the effect of mismatched line impedance, where the virtual powers rather than the ordinary P/Q are used in the droop equation. In case the output voltage violates the operation code, a restoration mechanism is proposed to reset V̇ to zero. Compared with existing virtual impedance and Q - V̇ droop methods, the proposed method combines the advantages of both. Besides, a modified P - V̇ strategy is also presented to accelerate the restoration process and improve the active power-sharing accuracy at the same time. Simulation results validate the effectiveness of the proposed method.

Voltage regulation is imperative for the successful operation of electricity distribution networks, especially with a high penetration level of photovoltaic (PV) systems. Power compensation control (PCC) that uses both reactive power compensation and active power curtailment has shown promising results in alleviating voltage rise problems. It crucially relies on real-time communications among distributed PV systems. However, the transmission of state measurements and control signals in PCC is hampered by inevitable communication delays. Therefore, it is important to not only estimate the maximum tolerable communication delay (MTCD) but also develop an alternative technique for PCC under abnormal communication delay (ACD) conditions. This article presents a delay-tolerant predictive PCC for voltage regulation in distribution feeders. After estimating the MTCD based on voltage and power mutation, it uses normal PCC for effective operation when communication delay is within MTCD, or switches to predictive PCC under ACD conditions. An accurate prediction is achieved using a double neural network with online adjustment of weights and samples. Simulations on a sample distribution network demonstrate the effectiveness of our presented approach.

High penetration and fluctuation of renewable energy resources are threatening the voltage security of the integrated hybrid energy system (IHES). This article focuses on providing a fully distributed method to ensure the voltage security and information privacy of the IHES. First, a multiagent system based control scheme is presented, in which the upper level agent is responsible for the voltage security of the whole system, and the local dynamic performance of each distributed energy resource (DER) unit is formulated in the lower level unit agent. Second, a distributed model predictive control (DMPC) based coordinated voltage control method is proposed, by which only partial information exchange is needed in the interaction. Furthermore, the diverse requirements on voltage quality of each microgrid in the IHES are considered in this article and it provides a novel paradigm in this field compared with the traditional methods. To study the stability of the resultant DMPC, the Nash equilibrium is achieved and theoretically proved to ensure the convergence of the proposed method. Finally, the validity of the proposed method is verified by virtue of the simulation and experimental test results.

Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution ( LR ) and the corresponding high-resolution ( HR ) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a kernel locality-constrained coupled-layer regression (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L1L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

Relative states between neighbors are ubiquitous in large-scale systems, and the relative state saturations need to be considered when designing controllers based on this relative state information. This article investigates the consensus control of the delayed multiagent systems under the relative state saturations. By means of an incidence matrix, the consensus under the relative state saturations is transformed into the stability of edge dynamics operating on constrained sets. To enable the edge states to stay within the constrained sets, a novel nonlinear protocol is designed by embedding an elaborate saturation function. Sufficient conditions are identified, under which not only consensus is achieved but also relative state saturations do not occur. Moreover, this analytical result can address the consensus problem while preserving connectivity, within the limited communication range framework. Simulations illustrate the theoretical results.

This article investigates the consensus tracking of exponentially unstable multiagent systems with time-varying input delay. The truncated predictor feedback approach is utilized for designing delay-dependent state and output feedback protocols. And the explicit conditions that can realize consensus tracking are established in terms of parametric Lyapunov equations and scalar inequalities. Besides, the protocol design algorithms under the maximum allowable delay and the maximum convergence rate are, respectively, provided. Compared with the existing results, the salient characteristic of the current results is to reveal the quantitative relationship among the time delay, unstable plant, network topologies, and the convergence rate. Specifically, for achieving the consensus tracking, the synchronization force from network connectivity needs to dominate the anti-synchronization force from unstable open-loop poles and input delays; the maximum allowable input delay is inversely proportional to the sum of the unstable open-loop poles; the convergence rate becomes smaller as the input delay bounds or/and the sum of the unstable poles in plant increase. Numerical simulations confirm the effectiveness of the proposed theoretical design.

Within the multiagent systems framework, the relative states between neighbors can be acquired by some on-board sensors, and then the relative state saturations inevitably occur due to the limited sensing capabilities. This paper investigates the consensus problem of nonlinear multiagent systems subject to the relative state saturations. Utilizing the incidence matrix and the edge Laplacian, the consensus problem of nonlinear multiagent systems with the relative state saturations can be cast into the stabilization problem of edge dynamics operating on the constrained set. Three types of protocols, namely continuous, intermittent, and adaptive state feedback protocols are, respectively, proposed for achieving the constrained consensus, and meanwhile yielding consensus values. A consensus analysis is provided by virtue of state saturation theory, switched system theory, adaptive theory, and Lyapunov stability theory. Output feedback protocol is also designed. Finally, the obtained results are applied to connectivity preservation for first-order nonlinear multiagent systems, despite the presence of limited communication range and input constraints. The theoretical results are validated by two simulation examples.

This article studies the security of networked interconnected systems (NISs) subject to cyber-attacks based on a new event-triggered mechanism (ETM). NISs with spatially distributed subsystems are vulnerable to cyber-attacks. With a new concept of security control, attention is focused on designing a novel ETM together with a decentralized output feedback control (DOFC) scheme such that the NIS subject to cyber-attacks is stable in secure sense. Under the proposed ETM, the average data-releasing rate over the whole operating period can be extremely decreased, thereby reducing the burden of network bandwidth, computation, and battery-supply. Moreover, during the system with external disturbance or attack on the communication network, more transmission-events can be generated than other periods. As a result, the desired control performance can be achieved. By using stochastic analysis techniques and Lyapunov stability theory, sufficient conditions are derived to obtain both the controller gains and the parameters of the ETM. Numerical simulation of chemical reactor systems is given to illustrate the advantages and effectiveness of the proposed theories and design techniques.

This note is concerned with bandwidth allocation-based switched dynamic triggering control under DoS attacks and time delay. To prevent DoS attacks from causing open-loop unstable operation of the system with single-channel transmission, we present a primary-redundancy (PR) communication structure on the basis of limited bandwidth allocation; the correlation of communication delay bound between PR channels is introduced. To save the limited bandwidth, we propose a dynamic the event-triggered mechanism (DETM). Then, a switched delay system model is established to describe system dynamic driven by primary channel control, redundancy channel control, and unstable operation under DoS attacks. Further, by using the convex combination method, a switching law for PR channel is designed with the aim of keeping the concealment of the redundancy channel. With the switching law, a criterion of exponential stability is obtained by using piecewise Lyapunov-Krasovskii functional method. A co-design method is proposed to obtain feedback control gains, DETM parameters and switching law parameters. Finally, two simulation examples are illustrated to verify the validness of our proposed method.

This paper focuses on the resilient H∞filter design for event-triggered networked systems subject to nonperiodic denial-of-service (DoS) jamming attacks. In this paper, a new resilient event-triggered transmission strategy is first proposed to improve the efficiency of network resource utilization while counteracting the nonperiodic DoS jamming attacks. Then, by using a time-delay approach, the filtering error system is modeled as a switched system, which characterizes the effects of the event-triggering scheme and nonperiodic DoS jamming attacks simultaneously. Based on the established model, by using the piecewise Lyapunov-Krasovskii functional method, linear matrix inequality (LMI)-based sufficient conditions are formulated to achieve the globally exponential stability as well as the weighted H∞performance of the resulting switched system under the DoS jamming attacks. Consequently, the co-design method for the desired filter parameters and event-triggering parameters can be formulated provided that the above LMIs are feasible. Finally, the effectiveness of the proposed method is demonstrated by a practical example.

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 article investigates resilient event-triggered load frequency control (LFC) of multiarea power systems under nonideal network environments. Under the sample-data framework, a novel QoS-dependent event-triggered communication (QEC) scheme is presented to deal with nonideal network environments while preserving the desired control performance and improving the communication efficiency. In comparison with some traditional state-dependent event-triggered communication schemes, since the proposed QEC depends not only on the state of controlled plant but also on the QoS of communication network, higher communication efficiency can be achieved. Then, a resilient LFC is well developed based on the proposed QEC, where “resilient” implies that: 1) for a normal QoS case, less packets are transmitted to save the communication resources and 2) for an abnormal QoS case, more packets are transmitted to mitigate the influence of nonideal QoS. Moreover, the proposed method provides a better way to balance the control performance and communication resource by reasonably choosing the parameters of QEC. Finally, the simulation results show the effectiveness of the proposed method.

Containment control is an important issue in the consensus problem of multiagent systems (MASs). This article presents an output-based containment control strategy for a class of nonaffine nonlinear MASs with uncertainies and directed topology. With the help of differential homeomorphism transform and the idea of active disturbance rejection control (ADRC), a nonaffine nonlinear MAS is transformed into an affine one with uncertainties. Two filters with extended states in each follower are designed with the idea of extended state observer to reconstruct the states of the transformed MAS. Then, uncertainties of the MAS are compensated with the help of the estimations of the extended states. Moreover, the derivative of the virtual control signal in the backstepping technique is replaced by a tracking differentiator (TD), overcoming the so-called “explosion of complexity” problem. By means of Lyapunov theory, the containment errors of the followers are proven to converge to a small neighborhood around the origin via an appropriate choice of parameters. Simulation examples are provided to demonstrate the proposed control strategy.

An observer-based containment control issue is addressed for a class of uncertain nonlinear multiagent systems with a directed topology via the active disturbance rejection control and backstepping techniques. A kind of nonlinear extended state observers (ESOs) based on fractional power functions is developed, and the estimations of extended states are utilized to compensate uncertain dynamics in real time. Compared with linear ESOs, the advantages of the ESOs in this paper lie in peaking reduction and better tolerance of measurement noise for the closed-loop system. Moreover, tracking differentiators are employed to avoid the explosion of complexity caused by repeated differentiations of nonlinear functions. It is proven that the containment errors of the followers converge to small neighborhoods of the origin and they are adjustable by suitable choice of parameters. Finally, two simulation examples, both practical and numerical ones, are shown to demonstrate the effectiveness of the proposed control approach.

This article considers integral sliding-mode control (ISMC) for multiagent systems with matched disturbances through adaptive dynamic programming (ADP) method. A distributed disturbance observer (DDO) is designed for each agent to generate the disturbance estimation. Using the disturbance estimation, an ISMC scheme is proposed to reject the input disturbance and obtain the equivalent local neighbor consensus sliding-mode dynamics. Then, a discount performance function is designed for each agent, and ADP technique is utilized to address the optimal consensus control for the equivalent sliding-mode dynamics online. Based on the gradient descent algorithm, we propose the adaptive weight tuning laws for the critic-actor neural networks (NNs) to carry out the ADP method. Furthermore, the local neighbor consensus errors and the weight estimation errors for the critic-actor NNs are proved to be uniformly ultimately bounded (UUB). Finally, the practical example is applied to validate the effectiveness of our results.

Hybrid and intermittent characteristics of the distributed energy resources (DERs) bring great challenges to the security control and economic dispatch (ED) of the microgrids. To bypass these hurdles, this article proposes a multiagent system-based integrated design of security control and ED to guarantee the effective and economical operation of the interconnected microgrids. First, a hierarchical control scheme is constructed by two-level unit agents, in which the switching control and dynamic regulation are fully implemented with the corresponding hybrid behaviors based on the differential hybrid Petri-net (DHPN) model. Based on the DHPN model, a novel dynamic ED integrated with security control is proposed to overcome the issues that cannot be solved in conventional models. Furthermore, to reduce the computational complexity and unified the mathematical model of the DERs, the inverter-based power control strategy is converted to a predictive control model which can be decomposed into several subsystems. In the optimization process, all the subsystems are implemented in a fully distributed, communication free, and rolling optimization manner based on the distributed model predictive control (DMPC). The validity of the proposed design is demonstrated according to the simulation results in case studies.

The stochastic nature of intermittent energy resources has brought significant challenges to the optimal operation of the hybrid energy systems. This article proposes a probabilistic multiobjective evolutionary algorithm based on decomposition (MOEA/D) method with two-step risk-based decision-making strategy to tackle this problem. A scenario-based technique is first utilized to generate a stochastic model of the hybrid energy system. Those scenarios divide the feasible domain into several regions. Then, based on the MOEA/D framework, a probabilistic penalty-based boundary intersection (PBI) with gradient descent differential evolution (GDDE) algorithm is proposed to search the optimal scheme from these regions under different uncertainty budgets. To ensure reliable and low risk operation of the hybrid energy system, the Markov inequality is employed to deduce a proper interval of the uncertainty budget. Further, a fuzzy grid technique is proposed to choose the best scheme for real-world applications. The experimental results confirm that the probabilistic adjustable parameters can properly control the uncertainty budget and lower the risk probability. Further, it is also shown that the proposed MOEA/D-GDDE can significantly enhance the optimization efficiency.

This paper is concerned with the problem of load frequency control for multi-area power systems with renewable energy sources penetration which increase the instability of the interconnected power system because of system inertia reduction. In order to tackle this problem, by using the virtual inertia control technique, a new load frequency control model has been proposed to improve frequency stability of the interconnected power system due to the negative impact causing by renewable energy sources. Firstly, load frequency scheme with network induced delays is formulated for multi-area power systems. Secondly, by using the Lyapunov theory and linear matrix inequalities (LMIs) technique, stabilization criteria and H∞controller for the multi-area power systems are derived. Finally, a case study demonstrates the effectiveness and merits of the present method.

This paper is concerned with the observer-based event-triggered control for a continuous networked linear system subject to denial-of-service (DoS) attacks, where the attacks are launched periodically to block the data transmission in control channels. First, a new observer state-based resilient event-triggering scheme is developed in the presence of DoS attacks. Second, a novel event-based switched system model is established by considering the effect of the event-triggering scheme and DoS attacks simultaneously. By virtue of this new model combined with a piecewise Lyapunov-Krasovskii functional method, the sufficient conditions are derived to guarantee exponential stability of the resulting switched system. It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate. Third, criteria for designing a desired observer-based event-triggered controller are provided and expressed in terms of a set of linear matrix inequalities. Finally, an offshore structure model is presented to illustrate the efficiency of the developed control method.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

This paper proposes an improvement on observer design of discrete-time fuzzy systems based on an alterable weights method. Different from the recent result, a more effective ranking-based switching mechanism is developed by introducing a bank of alterable weights for the sake of making use of the size difference information of the normalized fuzzy weighting functions more freely than before. Therefore, a positive result can be provided in this paper, that is, less conservative conditions of designing feasible fuzzy observers can be obtained than those existing results, while the computational cost of designing feasible fuzzy observers is even less than the up-to-date one. Finally, two numerical examples are given to show the progressiveness of the proposed method.

The problem of observer-based state estimation of discrete-time fuzzy systems is investigated by constructing a joint switching mechanism for adjacent instants. Thanks to the usage of both the proposed spatial partitioning method and a set of new free matrix-valued variables, abundant information about size differences of all the normalized fuzzy weighting functions for adjacent instants can be interactively integrated into the fuzzy observer design for the first time. Compared with the recent result from three important aspects (conservatism level, online computational burden, and offline computational burden), three positive results can be obtained: 1) it provides a chance for reducing the conservatism by a large margin; 2) the required online computational burden remains unchanged as referred ones; and 3) as a tradeoff, the required offline computational burden increases to some extent but is still affordable from the view of complexity analysis. Finally, two numerical simulations have been given to validate the effectiveness of our developed theoretical results.

This paper addresses an adaptive event-triggered consensus control problem for a class of second-order nonlinear multiagent systems (MASs) in an undirected communication topology. A novel adaptive distributed event-triggered consensus control scheme is presented for the MAS with unknown functions based on the definition of an auxiliary state, and the coefficient of the triggered function can be regulated adaptively with dependence on the auxiliary state error to ensure not only the control performance but also the efficiency of the network interactions. Furthermore, two self-triggered algorithms are developed for two cases, known functions and unknown ones, by the current state and information at the previous event time instant instead of the requirement for continuous monitoring auxiliary state errors. In theory, the stability of the resulting closed-loop system is rigorously investigated, and it is proven that all signals in the closed-loop system are bounded and the Zeno behavior is ruled out. Finally, two simulation examples, both real-time and numerical ones, are provided to verify the theoretical claims.

This paper investigates the output feedback stabilization problem for networked control systems under a stochastic scheduling protocol. First, an independent and identically distributed (i.i.d) scheduling protocol is introduced to orchestrate the signal transmission via a communication network. Taking into account the i.i.d protocol, network-induced delay, and packet dropout, a stochastic impulsive delayed model is presented for the studied system. Second, by use of the Lyapunov-Krasovskii functional approach, sufficient conditions for guaranteeing the stability of the studied system in the mean-square sense are obtained in the form of matrix inequalities. Moreover, an optimization algorithm is investigated to obtain the suitable dynamic output feedback controller and optimal i.i.d protocol parameters simultaneously. Finally, two numerical examples are presented to show the validity of the proposed method.

This paper investigates finite-horizon optimal consensus control problem for unknown multiagent systems with state delays. It is well known that optimal consensus control is the solutions to the coupled Hamilton-Jacobi-Bellman (HJB) equations. An off-policy reinforcement learning (RL) algorithm is developed to learn the two-stage optimal consensus solutions to the coupled time-varying HJB equations using the measurable state data instead of the knowledge of the state-delayed system dynamics. Subsequently, for each agent, a single critic neural network (NN) is utilized to approximate the time-varying cost function and help to calculate optimal consensus control policy. Based on the method of weighted residuals, adaptive weight update laws for the critic NNs are proposed. Finally, the simulation results are provided to illustrate the effectiveness of the proposed off-policy RL method.

Rough K-Means (RKM) is an efficient clustering algorithm for overlapping datasets, and has captured increasing attention in recent years. RKM algorithms are the main focus on the further description of uncertain objects located in boundary regions in order to improve the performance. However, most available RKM algorithms fail to pay attention to the influence of imbalanced clusters, together with imbalanced spatial distributions (i.e., the cluster density) and differing cluster sizes (i.e., the number of object ratios). This paper seeks to address this deficiency and examines in detail some adverse effects caused by imbalanced clusters. To mitigate adverse effects of imbalanced clusters and decrease the computational cost, an interval type-2 fuzzy local measure for the RKM clustering is proposed, on the basis of which, a novel RKM clustering algorithm has been developed that specifically gives due consideration to imbalanced clusters. The effectiveness and superiority of this algorithm are demonstrated through simulation and experimental analysis.

This article addresses the problem of distributed resilient finite-time control of multiple heterogeneous battery energy storage systems (BESSs) in a microgrid subject to denial-of-service (DoS) attacks. Note that DoS attacks may block information transmission among BESSs by preventing the BESS from sending data, compromising the devices and jamming a communication network. A distributed secure control framework is presented, where an acknowledgment (ACK)-based attack detection strategy and a communication recovery mechanism are introduced to mitigate the impact of DoS attacks by repairing the paralyzed topology graphs caused by DoS attacks back into the initial connected graph. Under this framework, a distributed resilient finite-time secondary control scheme is proposed such that frequency regulation, active power sharing, and energy level balancing of BESSs can be achieved simultaneously in a finite time; meanwhile, operational constraints can be satisfied at any control transient time. Moreover, based on theoretical analysis, the impact of the duration time of DoS attacks on the convergence time of the control algorithm can be explicitly revealed. Finally, validity and effectiveness of the proposed control scheme are demonstrated by case studies on a modified IEEE 57-bus testing system.

Due to the uncertainty of intermittent energy and system load, it is a big challenge to optimally operate an isolated power system. This article proposes a two-stage optimal operation strategy with a Takagi–Sugeno–Kang (TSK) fuzzy system to address the supply security under uncertainty circumstance. For proper analysis of the uncertainty characteristics, adjustable uncertainty parameters of intermittent energy resource and system load are taken as fuzzy sets; with the consideration of the robustness of these uncertainty parameters on isolated power system, it creates a supply-security identification model with the TSK fuzzy approach under radial basis function (RBF) neural network, and deduces optimal weight values with a recursive least square method. For properly avoiding potential risks, security index is classified into several degrees, each degree of risk can switch a different operation model, which can ensure the supply security of an isolated power system. For properly solving the optimization model, gradient descent-based multiobjective cultural differential evolution is employed to minimize economic cost and emission rate simultaneously. With simulations on isolated regional network, the obtained results reveal that the proposed method can be a viable alternative for optimal operation in isolated power systems.

Within the framework of multiagent systems, relative information can be directly acquired by vehicle-mounted sensors and the relative information constraints inevitably occur due to limited sensing capabilities. This paper investigates observer-based consensus of nonlinear multiagent systems subject to relative state estimate constraints. Each agent's state is constructed via a state observer, and the relative state estimate is assumed to be confined into a hypercube. In virtue of the edge Laplacian, the consensus problem of nonlinear multiagent systems under this constraint is converted into the stabilization problem of edge dynamics operating on the constrained set. Observer-based intermittent protocol and adaptive protocol are, respectively, designed for achieving consensus. A convergence analysis is provided with the help of state saturation theory, switched system theory and adaptive theory. Finally, the results on consensus with relate state estimate constraints are applied into the consensus problem while preserving the connectedness, and are validated by a simulation example.

This paper investigates leaderless consensus and leader-following consensus of multiagent systems with Lipschitz nonlinearity and input delay. For such systems, the observer-based truncated prediction feedback protocols are designed via dropping the distributed term and remaining the exponential term of the solution of the system equation over the delay period. Using Lyapunov-Krasovskii functional approach, two sufficient criteria are, respectively, established for achieving leaderless and leader-following consensus. The observer and controller gains are then obtained by means of an iterative linear matrix inequality procedure. A numerical simulation verifies the effectiveness of the proposed control design approach.

This paper addresses the problem of reducing the conservatism for stabilization of discrete-time Takagi-Sugeno fuzzy systems via a new extended representation approach. Different from previous slack variable methods, an advanced slack variable method without limitations is proposed for further making good use of available fuzzy weighting functions. Owing to the introduction of extended representations for homogenous polynomials, those strong limitations in recent literature are relaxed and thus the primary and secondary sequences of all normalized fuzzy weighting functions can be mapped to one augmented multi-indexed matrix for each homogeneous polynomial. Therefore, less conservative stabilization conditions can be provided as a result of these effective measures and thus a part of the online computational cost is transferred to the offline implementation. Finally, the benefits of the proposed method are illustrated by means of numerical experiments.

In this paper, we address the issue of the prescribed performance control of a class of pure-feedback nonlinear systems with uncertainties and input saturation. The active disturbance rejection control is adopted at each step of the backstepping technology. In detail, the extended state observer is employed to estimate unknown functions to compensate for uncertain items. The tracking differentiator is used to take place of the derivative of the virtual control signals, which overcomes the repeated differentiations of nonlinear functions. The control input limitation is handled with the auxiliary system, and predefined tracking performance functions are utilized to improve the tracking accuracy and speed. By means of the input-to-state stability and Lyapunov stability theory, the tracking error is proven to converge to arbitrarily small neighborhood of the origin. Two simulation examples are provided to demonstrate the effectiveness of the presented results.

This paper is concerned with the distributed secondary control problem of multiple battery energy storage systems (BESSs) in an islanded microgrid, where the dynamics of each battery is heterogeneous. It is assumed that each battery can communicate with its neighbors via communication networks whose communication topologies are switching over time. A distributed finite-time secondary control scheme is proposed to ensure frequency regulation, active power sharing and energy level balancing of BESSs in a finite time, while operational constraints can be satisfied at any control transient time. Finally, validity and effectiveness of the proposed control scheme are demonstrated by case studies on a modified IEEE 57-bus testing system.

This paper is concerned with resilient H∞ triggering control for discrete-time networked control system with parameter uncertainty and aperiodic DoS attacks. First, a switched time delay system model in discrete domain is established to describe the dynamic of discrete triggering control system subjected to DoS attacks. To analyze the H∞ performance of the switched delay system, we combine discrete piecewise Lyapunov-Krasovskii functional method and average dwell time (ADT) based switch system method. Then, the weighted H∞ performance would be preserved if the derived sufficient conditions were satisfied. The main conclusion is a criterion that the allowable margin of the duration and frequency of DoS attacks can be estimated explicitly. Under the criterion, the static output feedback control law can be solved by using LMIs techniques. Finally, we apply our method to design the resilient load frequency control (LFC) scheme of a three-area power system.

This paper focuses on the issue of designing an adaptive event-triggered scheme to the decentralized filtering for a class of networked nonlinear interconnected system. A novel adaptive event-triggered condition is proposed by constructing an adaptive law for the threshold. This new type of threshold mainly depends on the error between the states at the current sampling instant and the latest releasing instant, by which the data release rate is adapted to the variation of the system. The limitation of network bandwidth is alleviated on account of a large amount of "unnecessary" packets being dropped out before accessing the network. Sufficient conditions are derived such that the overall filtering error system under the proposed adaptive data-transmitting scheme is asymptotically stable with a prescribed disturbance attenuation level. An example is given to show the effectiveness of the proposed scheme.

In this paper, the event-based controller synthesis problem for networked control systems under the resilient event-triggering communication scheme (RETCS) and periodic denial-of-service (DoS) jamming attacks is studied. First, a new periodic RETCS is designed under the assumption that the DoS attacks imposed by power-constrained pulsewidth-modulated jammers are partially identified, that is, the period of the jammer and a uniform lower bound on the jammer's sleeping periods are known. Second, a new state error-dependent switched system model is constructed, including the impacts of the RETCS and DoS attacks. According to this new model, the exponential stability criteria are derived by using the piecewise Lyapunov functional. In these criteria, the relationship among DoS parameters, the triggering parameters, the sampling period, and the decay rate is quantitatively characterized. Then, a criterion is also proposed to obtain the explicit expressions of the triggering parameter and event-based state feedback controller gain simultaneously. Finally, the obtained theoretical results are verified by a satellite yaw-angles control system.

The distributed cooperative control for frequency and voltage stability and power sharing in microgrid considering the limitation of communication network is concerned in this paper. Two types of novel event-triggered mechanism with distributed architecture are first proposed, which can greatly reduce the communication burdens among power source inverters. Based on the event-triggered schemes, distributed restoration mechanism is constructed, which can restore the frequency and voltage magnitude of microgrid and realize the fair utilization of all power sources with comparative less requirements for the transmission data. Simulation is carried out to verify the effectiveness of the proposed method.

This paper is concerned with data-driven distributed optimal consensus control for unknown multiagent systems (MASs) with input delays. The input-delayed MAS model is first converted into a delay-free form using a model reduction method. By establishing an equivalent relationship on the predesigned performance indices of the two MASs, optimal consensus control of input-delayed MAS can be fully transformed to that of delay-free MAS. Based on the coupled Hamilton-Jacobi equations and Bellman's optimality principle, optimal consensus control policies are derived for the transformed delay-free MAS. Then a policy iteration algorithm based on distributed asynchronous update mechanism is proposed to learn the coupled Hamilton-Jacobi-Bellman equations online. To perform the proposed data-driven adaptive dynamic programming algorithm, we adopt the measured data-based critic-actor neural networks to approximate the value functions and the control policies, respectively. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

In this paper, we present a novel high-frequency facial feature and a high-frequency based sparse representation classification to tackle single sample face recognition (SSFR) under varying illumination. Firstly, we propose the assumption that QRCP bases can represent intrinsic face surface features with different frequencies, and their corresponding energy coefficients describe illumination intensities. Based on this assumption, we take QRCP bases with corresponding weighting coefficients (i.e. the major components of energy coefficients) to develop the high-frequency facial feature of the face image, which is named as QRCP-face. The normalized QRCP-face (NQRCPface) is constructed to further constraint illumination effects by normalizing the weighting coefficients of QRCP-face. Moreover, we propose the adaptive QRCP-face (AQRCP-face) that assigns a special parameter to NQRCP-face via the illumination level estimated by the weighting coefficients. Secondly, we consider that the differences of pixel images cannot model the intraclass variations of generic faces with illumination variations, and the specific identification information of the generic face is redundant for the current SSFR with generic learning. To tackle above two issues, we develop a general high-frequency based sparse representation (GHSP) model. Two practical approaches separated high-frequency based sparse representation (SHSP) and unified high-frequency based sparse representation (UHSP) are developed. Finally, the performances of the proposed methods are verified on the Extended Yale B, CMU PIE, AR, LFW and our self-built Driver face databases. The experimental results indicate that the proposed methods outperform previous approaches for SSFR under varying illumination.

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.

In this paper, we are concerned with the fault-tolerant control (FTC) issue for a class of large-scale uncertain multi-input and multioutput nonlinear systems. The features of such class of systems are that virtual control variables are in nonaffine pure-feedback form and actuator failures consist of both lock-in-place and loss of effectiveness. We develop an adaptive observer to reconstruct unavailable state information for this class of systems taking advantage of the universal approximation property of neural networks (NNs). And then, an observer-based decentralized adaptive FTC strategy is designed recursively by combining backstepping methods with NNs, FTC theory as well as the dynamic surface control (DSC) technique. The superiorities of this proposed strategy are that it is only dependent on output information of the system and there is no requirement for accurate parameters of the system. It is also hardly inevitable that repeat differentiation calculations of virtual functions with the help of DSC technology. In theory, the stability of the resulting closed-loop system is rigorously investigated, and it is proven that all signals remain uniformly ultimately bounded and tracking errors converge to a small neighborhood around the origin by suitable choice of design parameters. Finally, simulation results, both practical and numerical examples, are illustrated to verify the feasibility of the theoretical claims.

Energy Internet is recognized as a new and advanced paradigm of smart grids, where energy collection devices, distributed energy storage devices, and various types of energy nodes are interconnected by applying advanced power electronics technology, information technology, and intelligent management technology. This emerging Energy Internet brings remarkable improvement for the society from various aspects with its advantages in high efficiency, strong flexibility, great scalability, and improved reliability. However, it in turn generates new challenges in architecture design, control operation, and energy management. As a result, how to deal with these challenges in Energy Internet still needs further investigation by applying advanced techniques, such as multiagent systems, artificial intelligence-based control, big data cloud computing and management, and so on.

Due to the strong intermittency and volatility and the increasing proportion of photovoltaic (PV) power in the power grid, the PV power prediction becomes more and more important for the reliability of the power grid. Neural network is a popular model that used for PV power prediction. However, traditional neural networks prediction model that relies solely on the offline training cannot adapt well to the dynamic changes of PV power station. To cope with this problem, the very short-term online forecasting model for PV power based on two-stage resource allocation network (RAN) is presented. Firstly, the RAN model is offline trained to determine the initial structure. Thereafter, the initial RAN model is used for online forecasting, in this stage, the forecasting model is further updated. The simulation results show that the two-stage RAN model can effectively improve the forecasting accuracy of the PV power output.

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

The problem of relaxed real-time scheduling stabilization of nonlinear systems in the Takagi-Sugeno fuzzy model form is studied by proposing a new alterable-weights-based ranking switching mechanism. Thanks to the proposed alterable-weights-based ranking switching mechanism, a new fuzzy switching controller is developed with a set of activated modes that are adjusted by the real-time joint distribution of normalized fuzzy weighting functions. It is worth noting that those existing real-time scheduling stabilization results can be improved without introducing additional offline computational burden while solving control gain matrices. More importantly, less conservative stabilization conditions lead to a smaller degree of the fuzzy homogenous polynomially parameter-dependent switching controller, and thus, less online computational burden is required in the actual application. The effectiveness and superiority of the proposed method are verified by two simulation examples in the numerical section.

This paper deals with more relaxed designs of discrete-time Takagi-Sugeno fuzzy-model-based observers via two effective technical measures. Different from previous methods of this field, two effective technical measures are developed for obtaining more relaxed results, i.e., 1) A novel ranking-based switching mechanism is proposed by introducing a set of weighted variables so as to make use of the size differences information among normalized fuzzy weighting functions more freely. 2) A new slack variable method is proposed via the introduction of some extended representations for homogenous polynomials, and those major and minor relationships among all normalized fuzzy weighting functions are mapped to sole augmented multiindexed matrix for each homogeneous polynomial. Therefore, two positive results are provided in this paper, i.e., less conservative fuzzy observers can be given as a result of the above effective measures whilst a part of the online computational cost can be transferred to the offline implementation. Finally, two numerical examples are applied to illustrate the effectiveness of our approach.

This paper focuses on a problem of event-triggered stabilization for a class of nonuniformly sampled neural-network-based control systems (NNBCSs). First, a new event-triggered data transmission mechanism is designed based on the nonperiodic sampled data. Different from the previous works, the proposed triggering scheme enables the NNBCSs design to enjoy the advantages of both nonuniform and event-triggered sampling schemes. Second, under the nonperiodic event-triggered data transmission scheme, the nonperiodic sampled-data three-layer fully connected feedforward neural-network (TLFCFFNN)-based event-triggered controller is constructed, and the resulting closed-loop TLFCFFNN-based event-triggered control system is modeled as a state delay system based on time-delay system modeling approach. Then, the stability criteria for the closed-loop system is formulated using Lyapunov-Krasovskii functional approach. Third, the sufficient conditions for the codesign of the TLFCFFNN-based controller and triggering parameters are given in terms of solvability of matrix inequalities to guarantee the asymptotical stability of the closed-loop system and an upper bound on the given cost function while reducing the updates of the controller. Finally, three numerical examples are provided to illustrate the effectiveness and benefits of the proposed results.

This paper is focused on the issue of scheduling stabilization of Takagi-Sugeno fuzzy control systems by the aid of digging down much deeper of implicit information in the underlying systems. An event-triggered real-time scheduler that decides which control mode should be executed at any given instant is constructed by periodically evaluating the joint-distribution-type of multi-instant normalized fuzzy weighting functions at every sampled instant. Profiting from the proposed event-triggered scheduling policy, the proper control mode for the current instant is updated in order to adapt time-varying situations once if the underlying joint-distribution-type changes, and thus previous implementations of control tasks with an unchanged control mode can be further relaxed in this paper. The effectiveness of our approach is verified by several simulation examples in the end.

The augmented multi-indexed matrix approach acts as a powerful tool in reducing the conservatism of control synthesis of discrete-time Takagi-Sugeno fuzzy systems. However, its computational burden is sometimes too heavy as a tradeoff. Nowadays, reducing the conservatism whilst alleviating the computational burden becomes an ideal but very challenging problem. This paper is toward finding an efficient way to achieve one of satisfactory answers. Different from the augmented multi-indexed matrix approach in the literature, we aim to design a more efficient slack variable approach under a general framework of homogenous matrix polynomials. Thanks to the introduction of a new extended representation for homogeneous matrix polynomials, related matrices with the same coefficient are collected together into one sole set and thus those redundant terms of the augmented multi-indexed matrix approach can be removed, i.e., the computational burden can be alleviated in this paper. More importantly, due to the fact that more useful information is involved into control design, the conservatism of the proposed approach as well is less than the counterpart of the augmented multi-indexed matrix approach. Finally, numerical experiments are given to show the effectiveness of the proposed approach.

This paper investigates the problem of robust fault estimation (FE) observer design for discrete-time Takagi-Sugeno fuzzy systems via homogenous polynomially parameter-dependent Lyapunov functions. First, a novel framework of the fuzzy FE observer is established with the help of a maximum-minimum-priority-based switching mechanism. Then, for every activated switching case, a targeted result is achieved by the aid of exploring an important property of improved homogenous polynomials. Since the helpful information of the underlying system can be duly updated and effectively utilized at every sampled point, the conservatism of previous results is availably reduced. Furthermore, the proposed result is further improved by eliminating those redundant terms of the introduced matrix-valued variables. Simulation results based on a discrete-time nonlinear truck-trailer model are provided to show the advantages of the theoretic result that is developed in this paper.

This paper generalizes recent results on multi-instant observer design for discrete-time Takagi-Sugeno fuzzy systems through a valid ranking-based switching approach. The approach hereby develops a concentrated subdivision of spanning space composed of normalized fuzzy weighting functions and then substantially produces a new ranking-based switching mechanism. By taking advantage of this ranking-based switching mechanism, a class of new fuzzy multi-instant observers are achieved and more relaxed design conditions with respect to the recent work can be obtained for ensuring the asymptotically stability of the developed state estimation error system. Two illustrative examples are provided to validate the effectiveness of the result given in this study.

This paper proposes multiagent system-based event-triggered hybrid controls for guaranteeing energy supply of a hybrid energy generation system with high security. First, a multiagent system is constituted by an upper level central coordinated control agent combined with several lower level unit agents. Each lower level unit agent is responsible for dealing with internal switching control and distributed dynamic regulation for its unit system. The upper level agent implements coordinated switching control to guarantee the power supply of overall system with high security. The internal switching control, distributed dynamic regulation, and coordinated switching control are designed fully dependent on the hybrid behaviors of all distributed energy resources and the logical relationships between them, and interact with each other by means of the multiagent system to form hierarchical hybrid controls. Finally, the validity of the proposed hybrid controls is demonstrated by means of simulation results in different scenarios.

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

Robust fault estimation (FE) observer designs for discrete-time nonlinear system are deeply discussed via a joint real-time scheduling law. Different from previous results involved in the field, a fresh FE observer is produced by means of intermittently launching a joint real-time scheduling law in the light of updated multi-instant information. Thanks to the overall vision of system information across several adjacent sampled points, less conservative result is favorably acquired. Second, the given result is ulteriorly ameliorated by effectively developing an improved introduction technique of free matrix variables. Finally, one nonlinear truck-trailer example is employed for validating the superiority of theoretic fruits that is given in this paper.

Position-patch based face hallucination approaches have been proposed to replace the probabilistic graph-based or manifold learning-based models recently. In this paper, we propose a novel position-based face hallucination method based on locality-constrained matrix regression (LcMR). LcMR uses nuclear norm to characterize the reconstruction error straightforward, thus preserving the essential structural information of the input. On the other hand, LcMR imposes a locality constraint onto the combination coefficients to reach sparsity and locality simultaneously. The locality constraint can derive an analytical solution to the optimization problem. Moreover, LcMR can be solved using alternating direction method of multipliers. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art approaches.

In real-word visual applications, distribution mismatch between samples from different domains may significantly degrade classification performance. To improve the generalization capability of classifier across domains, domain adaptation has attracted a lot of interest in computer vision. This work focuses on unsupervised domain adaptation which is still challenging because no labels are available in the target domain. Most of the attention has been dedicated to seeking domain-invariant feature by exploring the shared structure between domains, ignoring the valuable discriminative information contained in the labeled source data. In this paper, we propose a Dictionary Evolution (DE) approach to construct discriminative features robust to domain shift. Specifically, DE aims to adapt a discriminative dictionary learnt based on labeled source samples to unlabeled target samples through a gradual transition process. We show that the learnt dictionary is endowed with cross-domain data representation ability and powerful discriminant capability. Empirical results on real world data sets demonstrate the advantages of the proposed approach over competing methods.

This paper focuses on a problem of event-based stabilization for a class of sampled-data neural-network-based control systems. By using a new discrete event-triggering mechanism, an event-based sampled-data three-layer fully connected feedforward neural-network-based controller is constructed. Compared with the conventional periodic sampled-data neural-network-based control in previous works, the main advantage of this paper is that the proposed event-based sampling and transmission scheme not only reduces the updating frequency of the controller, but also guarantees the asymptotical stability of the closed-loop system without dramatically degrading the overall system performance. Based on a discontinuous Lyapunov Krasovskii functional, a convex combination technique and the Wirtinger-based integral inequality, some new criteria are derived to guarantee the asymptotical stability and certain performance of closed-loop system in terms of linear matrix inequalities (LMIs). Based on the proposed criteria, a co-design method is presented to obtain the triggering parameter and the connection weights of the neural network simultaneously while ensuring a certain system performance. Finally, simulation results are provided to show the effectiveness and advantage of the proposed theoretical results.

This paper deals with the problem of control synthesis of discrete-time Takagi-Sugeno fuzzy systems by employing a novel multiinstant homogenous polynomial approach. A new multiinstant fuzzy control scheme and a new class of fuzzy Lyapunov functions, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions, are proposed for implementing the object of relaxed control synthesis. Then, relaxed stabilization conditions are derived with less conservatism than existing ones. Furthermore, the relaxation quality of obtained stabilization conditions is further ameliorated by developing an efficient slack variable approach, which presents a multipolynomial dependence on the normalized fuzzy weighting functions at the current and past instants of time. Two simulation examples are given to demonstrate the effectiveness and benefits of the results developed in this paper.

Since a microgrid consists of various distributed energy resources and local loads, integration of a distributed grid and multiple microgrids leads to an integrated hybrid energy system. This paper focuses on improving profit, economy, security, and dynamic performance of the integrated hybrid energy system by means of developing different areas of management and control strategies upon four-level hierarchical multiagent system. The level 1 agent implements optimal price bidding strategies for high profit of the overall system. The level 2 agent optimizes the energy management strategies for economic operation of each microgrid. The level 3 agent executes coordinated switching control for maintaining security of each microgrid. The level 4 agent facilitates local hybrid control of each distributed energy resource for guaranteeing dynamic performance. Finally, validity of the proposed scheme is tested by means of simulation study.

This paper proposes a higher energy-efficient mixed sampling scheme (MSE) for networked control systems (NCSs) over IEEE 802.15.4 wireless networks. Compared with some existing periodic event-triggered sampling (ETS) schemes with a delayed sampling estimation, this delay is no longer existing in MSE since there is a dynamic adjustable threshold in MSE to compensate for this delay. Compared with some existing self-triggered sampling (STS)/ETS schemes, MSE does not require continuous measurement of the system states and does not suffer from the conservativeness induced by a self-triggered estimation. By using the proposed MSE, one can improve the energy efficiency in energy-constrained wireless NCSs (WiNCSs) by reducing the number of transmitted packets and increasing the idle-listening period of wireless sensor nodes. An inverted pendulum (Feedback 33-005-PCI) controlled over IEEE 802.15.4 wireless networks is given to demonstrate the effectiveness of the proposed method.

Focusing on the leak-points in petrochemical industries, this paper discusses the key factors (i.e., equipment temperature, gas pressure, and diffusion rate) in petrochemical industries. Data from sensors of petrochemical industries need to be timely operated because of time sensitivity and it is hard to achieve associated information from sensors located in production sites. To this end, we propose a three-level framework based on improved back propagation (TLBP). The real-time data streams are processed according to the arriving time in input layer. At the same time, a neuron-optimizing solution is introduced in learning process to deal with redundant and invalid neurons, thereby accelerating the response speed of learning and reducing the prediction time. Finally, we propose an improved mechanism of the multidimensional learning factor to lower the learning error and higher convergence rate. Meanwhile, to fulfill the distributed prediction on leak-points, we see one three-level data-processing unit as a logic machine with multiple operators. Using the assignment scheduling, the general scheduling problem is split into the common subproblem of every operator and the system overhead is reduced. With the processed data we can obtain the relative location or diffusion radius of leak-points, as well as the area of leak-points. Simulation results show that the TLBP performs better than related algorithms in different metrics. Besides, the adaptability of TLBP is verified in leak-points prediction of petrochemical equipment from the processed data.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification. For the given training image set which consists of HR gallery and LR probe images, we aim to convert the features of LR images into discriminating HR features. Specifically, our approach learns a pair of HR and LR dictionaries and a mapping from the features of HR gallery images and LR probe images. To ensure that the converted features using the learned dictionaries and mapping have favorable discriminative capability, we design a discriminant term which requires the converted HR features of LR probe images should be close to the features of HR gallery images from the same person, but far away from the features of HR gallery images from different persons. In addition, we apply low-rank regularization in dictionary learning procedure such that the learned dictionaries can well characterize intrinsic feature space of HR and LR images. Experimental results on public datasets demonstrate the effectiveness of SLD2L.

Due to the limited energy of sensors and the difficulties in battery replacement in DTMSN (Delay Tolerant Mobile Sensor Network), unbalanced energy consumption will exhaust the batteries of active sensors soon, which can significantly reduce the network lifetime. Fortunately, this problem can be solved through multi-hop relay transmission where those energy-aware sensors with the maximum remaining energy will be selected to forward packets. However, one of the major challenges of multi-hop relay transmission in DTMSN is how to schedule these mobile sensors travelling paths in an energy-balanced way so that their overall lifetime is maximized. In this paper, an energy-balanced multi-hop relay transmission scheme based on RVNS (Reduced Variable Neighborhood Search) in DTMSN is proposed. Firstly, several parameters are designed to calculate the remaining energy of each sensor. Then RVNS is applied to obtain the global optimal solution. RVNS is implemented to select a sensor node with maximum remaining energy as the next hop relay, delivering packets to destination through multi-hop relay transmission. Simulation results demonstrate that in a delay-tolerant condition, the proposed scheme significantly balances the energy consumption and improves the packet delivery ratio.

This paper investigates the problem of link scheduling in a single-hop energy harvesting wireless body area network (EH-WBANs) where sensor devices's energy harvesting rates and data rates are spatially heterogeneous and temporally variant. To maximize the channel utilization with the lifetime operation, an adaptive TDMA-based protocol (AT-MAC) is proposed, which is suitable for communication in an EH-WBAN for remote monitoring of physiological signals. In this protocol, a duty cycle can be dynamically adjusted to maintain the harvested energy amount, which is always greater than power consumption. Also, a novel time-slot allocation algorithm is designed to automatically adjust duty cycle with various data traffic and harvesting rates. This algorithm is decomposed into two sub-processes: predistribution of time-slot and redistribution of time-slot. The former process takes the information of energy harvesting rates into account, regardless of the rates varying in a nondeterministic manner and among various sensor nodes. For the latter process, the number of distributed time slots will be further adjusted to cope with data traffic of spatially heterogeneousness. Simulation results demonstrate the proposed protocal is a promising candidate for realizing the lifetime operation in EH-WBANs.

This paper studies the observer-based event-triggered predictive control problem for networked control systems (NCSs). First, we propose a discrete event-triggered transmission scheme for the observer by introducing a quadratic event-triggering function. Then, based on the above scheme, a novel class of event-triggered predictive control algorithms on the controller node are designed for compensating for the communication delays actively and achieving the desired control performance while using less network resources. The closed-loop systems with the proposed observer-based event-triggered predictive control scheme for the analysis are established correspondingly. The design problems of the controller and the event-triggering parameter are discussed by using the linear matrix inequality (LMI) approach and the switching Lyapunov functional method. Finally, a practical example is employed to demonstrate the compensation effect for the communication delays with the proposed scheme in this paper.

This paper addresses the event-triggered predictive control problem for networked control systems (NCSs). First, we propose a discrete event-triggered transmission scheme on the sensor node by introducing a quadratic function. Then, based on the above scheme, a novel class of event-triggered predictive control algorithms on the controller node are designed for compensating for the communication delays actively and achieving the desired control performance while using less network resources. Two cases in terms of the communication delays are considered respectively. For the two cases, the closed-loop systems with the proposed event-triggered predictive control scheme for the analysis are established correspondingly. The co-design problems of the controller and event-triggering parameter are discussed by using the linear matrix inequality (LMI) approach and the (switching) Lyapunov functional method. Finally, a practical example is employed to demonstrate the effects of the proposed scheme.

This paper is concerned with further studies on control synthesis of discrete-time Takagi-Sugeno (T-S) fuzzy systems. To do this, a novel slack variable technique, which is homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions with arbitrary degrees, is presented by developing an efficient augmented multi-indexed matrix approach. Under the framework of homogenous matrix polynomials, the algebraic properties of both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions are collected into sets of augmented multi-indexed matrices. Thus, more information about the underlying normalized fuzzy weighting functions is involved into control synthesis. Consequently, the relaxation quality of control synthesis of discrete-time T-S fuzzy systems is improved significantly. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

This paper presents relaxed stability and stabilization conditions for Takagi-Sugeno (T-S) fuzzy systems under network environments subject to asynchronous grades of membership. Because of the introduction of a communication network, the favorable property in the point-to-point connection, that is, sharing the identical premises in the fuzzy plant and the fuzzy controllers cannot be arbitrarily employed. To widen the applicability of the fuzzy control method under network environments, a novel method is provided to reconstruct the synchronous time scale grades of membership at the controller as those in the plant. As a result, the aforementioned favorable property can be conditionally used in the derivation of the stability and stabilization criteria for the system under consideration controlled over a communication network. Numerical examples have been given to illustrate the effectiveness of the proposed approach.

This paper is concerned with further studies on the control synthesis of discrete-time nonlinear systems in the Takagi-Sugeno (T-S) fuzzy form. To do this, a novel slack variable technique is presented by developing some useful matrix equalities, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions. Under the framework of homogenous matrix polynomials, the algebraic properties of both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions are collected for the first time into sets of united collection matrices. Consequently, the relaxation quality of control synthesis of discrete-time T-S fuzzy systems is improved, i.e., the convergence of asymptotically necessary and sufficient stabilization conditions is further sped up. Finally, a numerical example is provided to illustrate the effectiveness of the proposed result.

This paper is concerned with the stabilization and${H_\infty}$control problems for a class of singular networked cascade control systems with state delay and disturbance. Via the fact that single loop feedback control shows a shortcoming in that the plant output as nonzero disturbance acts on the systems, a new model of singular networked cascade control system is proposed. In this system, both the network-induced delay and data packed dropout phenomena are considered. Based on the model, sufficient conditions are derived in terms of linear matrix inequalities and the corresponding${H_\infty}$stabilizing controller design technique is also developed based on the above conditions. The proposed method emphasizes the implementation issue and employs the cascade control to singular networked control systems. A simulation example is given to illustrate the proposed design procedures and its applications.

This study considers robust H ∞ filtering for discrete system with uncertain multiple missing measurements and probabilistic non‐linearities. The measurement missing rate is assumed to be known with uncertainties and the non‐linearities’ variation information between different bounds is utilised to build new type of non‐linearity model. Employing these new characteristics, new filtering error dynamics with time delay and uncertainties are proposed. By using Lyapunov functional approach, sufficient conditions on the H ∞ performance analysis are obtained, and the desired filter parameters can also be obtained by solving certain linear matrix inequalities. Finally, an example is proposed to show the effectiveness of the proposed model and design procedures.

This paper first proposes a discrete event-triggered communication scheme for a class of networked Takagi-Sugeno (T-S) fuzzy systems. This scheme has two main features: 1) Whether or not the sampled state should be transmitted is determined by the current-sampled state and the error between the current-sampled state and the latest transmitted state. Compared with those in a periodic time-triggered communication scheme, the communication bandwidth utilization is considerably reduced while preserving the desired control performance; and 2) it is a discrete event-triggered communication scheme due to the fact that the triggered conditions are only measured and checked at a constant sampling period. Compared with a continuous event-triggered communication scheme, the special hardware for continuous measurement and computation is no longer needed. Second, a networked T-S fuzzy model is delicately constructed, which not only considers nonuniform time scales in the networked T-S fuzzy model and the parallel distributed compensation fuzzy control rules but includes the aforementioned state error as well. Third, a stability criterion and a stabilization criterion about the networked T-S fuzzy system are derived, respectively. The stability criterion and stabilization criterion can provide a tradeoff to balance the required communication resource and the desired performance: Lowering the desired performance allows the network to allocate more limited bandwidth to other nodes in need. Finally, a numerical example is given to show the effectiveness of the proposed method.

In this paper, we investigate the problem of stabilization for sampled-data fuzzy systems under variable sampling. A novel Lyapunov-Krasovskii functional (LKF) is defined to capture the characteristic of sampled-data systems, and an improved input delay approach is proposed. By the use of an appropriate enlargement scheme, new stability and stabilization criteria are obtained in terms of linear matrix inequalities (LMIs). Compared with the existing results, the newly obtained ones contain less conservatism. Some illustrative examples are given to show the effectiveness of the proposed method and the significant improvement on the existing results.

The system studied in this paper has four main features: 1) It is a networked controlled system (NCS), and therefore, the signal transfer is subject to random delay and/or loss; 2) it is a nonlinear system approximated by a Takegi--Sugeno (T-S) fuzzy model; 3) its multisensors and multiactuators are subject to various possible faults/failures; and 4) there are uncertainties in the plant model parameters. A comprehensive model is first developed in this paper to cover these features for a class of NCS nonlinear systems. This model has removed some limitations of similar models in the published literature. Then, the Lyapunov functional and the linear matrix inequality (LMI) are applied to develop two new stability conditions (Theorems 1 and 2). These conditions and an algorithm are used to design a controller to achieve robust mean square stability of the system. Finally, two examples are used to demonstrate the application of the modeling and the controller design method developed.

This correspondence paper addresses the problem of output feedback stabilization of control systems in networked environments with quality-of-service (QoS) constraints. The problem is investigated in discrete-time state space using Lyapunov's stability theory and the linear inequality matrix technique. A new discrete-time modeling approach is developed to describe a networked control system (NCS) with parameter uncertainties and nonideal network QoS. It integrates a network-induced delay, packet dropout, and other network behaviors into a unified framework. With this modeling, an improved stability condition, which is dependent on the lower and upper bounds of the equivalent network-induced delay, is established for the NCS with norm-bounded parameter uncertainties. It is further extended for the output feedback stabilization of the NCS with nonideal QoS. Numerical examples are given to demonstrate the main results of the theoretical development.

This paper investigates the robustHinfincontrol for Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delay. By employing a new and tighter integral inequality and constructing an appropriate type of Lyapunov functional, delay-dependent stability criteria are derived for the control problem. Because neither any model transformation nor free weighting matrices are employed in our theoretical derivation, the developed stability criteria significantly improve and simplify the existing stability conditions. Also, the maximum allowable upper delay bound and controller feedback gains can be obtained simultaneously from the developed approach by solving a constrained convex optimization problem. Numerical examples are given to demonstrate the effectiveness of the proposed methods.

This paper investigates robust stability analysis and stabilization of delay and uncertain systems approximated by a Takagi-Sugeno (T-S) fuzzy model. An innovative approach is proposed to develop delay-dependent stability criteria of the systems, which makes use of less-redundant information to construct Lyapunov function, employs an integral equation method to handle the cross-product terms, and alleviates the requirements of the bounding technique and model transformations that have been popularly adopted in many existing references. This leads to significant improvement in the stability performance with far fewer unknown variables in the stability computation. From the derived stability criteria, a new memoryless state-feedback control is further developed. The controller gain and the maximum allowable delay bound of the closed-loop control system can be obtained simultaneously by solving an optimization problem. Numerical examples are also given to demonstrate the theoretical results.

Motivated by the study of a class of networked control systems, this correspondence paper is concerned with the design problem of stabilization controllers for linear systems with stochastic input delays. Different from the common assumptions on time delays, it is assumed here that the probability distribution of the delay taking values in some intervals is knowna priori. By making full use of the information concerning the probability distribution of the delays, criteria for the stochastic stability and stabilization controller design are derived. Traditionally, in the case that the variation range of the time delay is available, the maximum allowable bound of time delays can be calculated to ensure the stability of the time-delay system. It is shown, via numerical examples, that such a maximum allowable bound could be made larger in the case that the probability distribution of the time delay is known.

In this paper, we are concerned with the problem of stability analysis and stabilization control design for Takagi-Sugeno (T-S) fuzzy systems with probabilistic interval delay. By employing the information of probability distribution of the time delay, the original system is transformed into a T-S fuzzy model with stochastic parameter matrices. Based on the new type of T-S fuzzy model, the delay-distribution-dependent criteria for the mean-square exponential stability of the considered systems are derived by using the Lyapunov-Krasovskii functional method, parallel distributed compensation approach, and the convexity of some matrix equations. The solvability of the derived criteria depends not only on the size of the delay but also on the probability distribution of the delay taking values in some intervals. The revisions of the main criteria in this paper can also be used to deal with the case when only the information of variation range of the delay is considered. It is shown by practical examples that our method can lead to very less conservative results than those by other existing methods.

This brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete-time recurrent neural networks (DRNNs) with stochastic delay. Different from the prior research works, the effects of both variation range and probability distribution of the time delay are involved in the proposed method. First, a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed DRNN model, where the delay is characterized by a stochastic binary distributed variable. Based on the new method, the global exponential stability in the mean square sense for the DRNNs with stochastic delay is investigated by using the Lyapunov-Krasovskii functional and exploiting some new analysis techniques. A numerical example is provided to show the effectiveness and the applicability of the proposed method.

Under the environment of network, the second-order inertial object is controlled when the single-packet transmission and no data packet dropout, the goal of this paper is to design controller of numeric PID without network delay that satisfy relevant performance and stabilizability when network-induced delay is included. The dynamic Smith controller and on-line time-delay identification are used; the specified methods of Smith parameter are purposed when the network delay is greater or less sample time and satisfy the given index. It can be used as the base for fuzzy compensation controller under multiple-packet and data packet dropout.