With the increasing digitalization and intelligence of smart grids, their reliance on communication and computing infrastructures has made them vulnerable to cyber threats, particularly false data injection attacks (FDIAs), which can severely compromise state estimation accuracy and threaten grid stability. Existing detection methods often face limitations in localization accuracy and recovery performance under varying attack intensities. To overcome these challenges, we propose a hybrid framework named ECN-Swin Transformer (EST), which combines an edge convolutional network (ECN) for capturing the grid’s topological features with a Swin Transformer for extracting both local and global temporal characteristics, thereby improving the detection and localization of FDIA-targeted nodes. Furthermore, for the recovery of compromised state variables, a SwinUNet model—an encoder-decoder architecture based on the Swin Transformer with U-Net-style skip connections—is developed to enable high-fidelity reconstruction under physical constraints. Extensive simulations on IEEE 14-, 39-, 118-, and 300-bus systems with varying FDIA intensities verify that the proposed EST+SwinUNet framework achieves superior localization accuracy, enhanced recovery quality, and better real-time adaptability compared to conventional methods, offering a robust and scalable solution for cyber-resilient smart grids.

The fight-or-flight response (FFR) is an instantaneous organismic response driven by emotions to external stimuli. However, most current intelligent systems requiring real-time environmental response overlooked this fundamental mechanism. Meanwhile, endowing systems with pre-response adaptive decision-making ability based on environment complexity (EC) considerations also necessitates in-depth research. This work proposes a decision-making model with a bionic memristive circuit comprising n FFR circuits and one environment enrichment (EE) module. The FFR circuit simulates FFR and implements long-term emotion memory (LTEM), memory forgetting, emotion generalization and fast emotion arousal. The EE module allows the circuit to dynamically adjust response targets in multi-stimulus scenarios by considering EC. Consequently, the circuit achieves adaptive and minimal delay responses to changeable stimuli via pre-response decision-making, validated by PSPICE simulations. The use of memristors facilitates online in-situ operating and in-memory computing, which makes the circuit expected to be deployed on multi-nozzle fire-fighting robots, enabling them to adaptively prioritize target processing in real time based on the urgency of various targets in multi-fire points scenarios.

For the frequency regulation and power allocation problem for ac microgrids, the multiple constraints of load frequency restrictions, nodal active power injections, and power-flow balance that guarantee transient stability are as crucial as the final consistent steady-state results. This article investigates a class of droop-controlled ac microgrids with the abovementioned constraints, and it presents sufficient and necessary conditions to improve system robustness and reliability under load fluctuations. By integrating the Kuramoto oscillator model into the primary control law, cyber-physical coupling dynamics for ac microgrids are established. A novel communication-network-based secondary distributed control approach is presented, which considers physical nodes with different characteristics of power generation and load units. By using invariant theory and nonquadratic Lyapunov techniques, the bounded input-output stability of microgrids can be guaranteed under certain conditions, which is especially pertinent in active power allocation. The theoretical results are verified through numerical case studies of both public and self-built power test systems, demonstrating the obvious improvement in robustness and reliability against variable power generation and load demand.

The growing integration of massive multi-source heterogeneous distributed generation (DG) into power grids and the emergence of cluster-collaboration-based generation modes have highlighted significant challenges in effectively controlling microgrid clusters (MGCs). This paper develops an interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model for the secondary control layer of MGCs with additive disturbances, designed to capture the dynamics of heterogeneous DG under non-cyber attacks. By adopting the constrained-tightening-based prescribed performance control strategy, the proposed low-computation distributed fuzzy secondary control protocol achieves voltage cooperative restoration for MGCs subject to additive disturbances. Notably, this protocol eliminates reliance on the dynamics of upper-level DG for lower-level communication networks while the efficient multi-layer communication structure is employed to facilitate cluster collaboration. Subsequently, the MGC security detection-isolation-deferred switching framework under distributed cyber attacks is established. The proposed method based on performance monitoring for security detection, node isolation, and performance recovery, demonstrating that multi-source heterogeneous DGs can be reconnected to the MGC through deferred switching. This approach enhances the feasibility of the design while maintaining MGC performance standards. Finally, a standard IEEE 33-node distribution model is employed to validate the proposed control protocol's effectiveness via the OPAL-RT real-time simulation platform.

In actual ac microgrids (MGs), the design of distributed secondary control methods requires information exchanges between distributed generators (DGs) by a communication network. This means MGs are potentially suffering from model-free Denial of Service (DoS) attacks and are affected by the constraints of transmission rate simultaneously. Accordingly, we develop a novel distributed security control strategy based on quantization to ensure the stable and economic operation of ac MGs. Considering DoS attacks can exacerbate the rate constraints inherent in communication networks, a quantization controller with a minimal quantization level is designed to account for extreme scenarios. This ensures that ac MGs achieves their control objectives by transmitting only 1 bit of data at each successful transmission instance while guaranteeing that the quantized signal does not exceed the range of quantizer throughout the entire process. In addition, the developed control strategy can synchronize the output voltage and frequency of DGs to their reference values, respectively, and achieve active power-sharing. Besides, the quantization level can be dynamically adjusted according to the actual situation of ac MGs to improve the operation effect of the algorithm. Finally, the performance of the proposed distributed security control strategy and comparisons with other research are evaluated on an ac MG composed of seven DGs in real-time testing equipment built on RT-LAB.

This article proposes a multiagent collaborative game evolution strategy to address issues of distributed resource sharing among virtual power plants, which is based on multicriteria matching and continuous learning. First, a peer matching mechanism is designed considering heterogeneous factor fusion and reasoning, which can enhance the adaptability and effectiveness of matching work. Then, a virtual–real hybrid game evolution framework is proposed based on continuous learning, in which a data-driven SE estimation method is developed to transform the traditional iterative response process into an efficient strategy estimation, thereby improving the efficiency of multiagent games. Furthermore, to further mitigate catastrophic forgetting in continuous learning, a multitimescale forward-feedback knowledge replay method is proposed to dynamically adjust replay timing and content, which can achieve knowledge consolidation through a combination of preventive maintenance and targeted repair, enhancing model evolution capabilities. Case studies validate that the proposed strategy exhibits superior performance in matching rationality, game convergence speed, and resistance to knowledge forgetting.

This article focuses on dynamic integral sliding mode control (SMC) of uncertain Takagi–Sugeno fuzzy delayed systems on time scales. SMC approaches for both continuous-and discrete-time fuzzy delayed systems are designed in a unified framework. First, we design a dynamic controller to guarantee global asymptotic stabilization (GAS) withH∞performance of the addressed systems. Second, to better adapt to the uncertainty characteristics of fuzzy models, an integral sliding mode surface (SMS) considering states, inputs, and uncertainties is proposed, which is an important contribution of this article. By utilizing the Lyapunov function and timescale calculus, it is shown that all states of the addressed control system can be driven close enough to the SMS and global asymptotic convergence (GAC) of the sliding motion can be ensured under matrix inequality criteria. In addition, the chattering phenomenon near the origin of discrete-time SMC system can be avoided in this article. Finally, three simulation examples are offered to illustrate the feasibility of the proposed control schemes.

This article proposes a novel distributed neuro-adaptive 3-D formation tracking control framework of multiple autonomous underwater vehicles (multi-AUVs) subject to marine environmental disturbances. On the one hand, we assume that all AUVs can obtain the real-time states. By introducing a series of variable transformations, the multi-AUV system is transformed into an underactuated nonlinear system with virtual control input. Radial basis function neural networks (RBFNNs), whose weights are updated online, are utilized to approximate nonlinear functions. Considering environmental disturbances, a virtual controller is designed such that all AUVs track the leader while maintaining the desired formation geometry. Then, the actual controller is given as an adaptive form according to the virtual control signals. On the other hand, we assume that all AUVs can only obtain the sampling states of themselves and their neighbors under the predefined event-triggered conditions. Multi-AUV system is transformed into a second-order system with complex nonlinear dynamics, then their states are reconstructed via a neuro-adaptive state observer using sampling states, and a virtual controller is proposed such that all AUVs track the leader while maintaining the desired formation geometry under local communication with no Zeno behavior. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed control design.

Solid Oxide Fuel Cell (SOFC) systems are increasingly deployed for distributed energy applications, ranging from kilowatt-scale residential systems to megawatt-scale industrial parks, due to their efficient and clean energy conversion characteristics. Accurate operational mode identification is essential for preventing stack damage, optimizing performance, and ensuring safety. However, existing methods struggle with cross-system transfer (varying power ratings and device configurations) due to disparities in feature distribution. To address this issue, this study analyzes the operational features of 1kW and 35kW systems, revealing the fundamental causes of the failures of traditional transfer methods, and proposes an innovative Domain-Adaptation Network designed specifically for large cross-system discrepancies. Our approach employs dedicated encoders for different systems, which share a common classifier, enabling effective knowledge transfer. Experiments demonstrate 98.98% accuracy in operational mode identification between 1kW and 35kW systems, with an inference time of 0.64ms, requiring only 10% of target domain data. Comparative experiments show that our approach significantly outperforms traditional domain adaptation methods, which achieve less than 10% accuracy, thereby reducing data collection costs and accelerating deployment cycles.

This paper focuses on addressing the practical finite-time synchronization (PFTS) problem of heterogeneous fractional-order complex dynamical networks (FCDNs) through event-triggered feedback control (ETFC). Firstly, a novel practical finite-time stability lemma is proposed based on the fractional-order differential inequality$_{t_{0}}^{C}D_{t}^{\alpha } V\left ({{ t }}\right) \le - {p_{1}}V\left ({{ t }}\right) - {p_{2}}{V^{\beta } }\left ({{ t }}\right) + q$, which plays a crucial role in analyzing PFTS. Secondly, a novel ETFC protocol is designed where the information transmission of the controller occurs at a sequence of state-dependent instants. Thirdly, using the aforementioned lemma and fractional Lyapunov theory, synchronization criteria for heterogeneous FCDNs can be derived, and Zeno behavior is excluded. Finally, the numerical example involving the PFTS of a fractional-order Lorenz’s circuit is provided to demonstrate the effectiveness of the proposed theoretical results.

Carbon trading mechanisms can constrain carbon emissions in energy supply, while demand response (DR) mechanisms have the potential to achieve low-carbon oriented energy consumption. Based on these, this paper focuses on mechanism-driven deep decarbonization methods of microgrid clusters (MGCs) from both source and load sides, and the key contributions are threefold. First, a novel dynamic vertical peer-to-peer carbon trading framework is proposed with profitability and fairness, in which a dynamic internal carbon emission right (CER) pricing mechanism is designed based on the supply–demand ratio of CERs and trading prices of the external primary carbon market. Then, a multiple structural DR mechanism is developed to increase the on-site utilization of renewable energy and decrease carbon emissions of MGCs, which considers the influence of electricity prices, the matching degree between renewable energy and loads, and these psychological preferences of users. Moreover, a DR feedback correction strategy is proposed to address the volatility of DR results caused by changes in load elasticity. Numerical results show that the proposed method can reduce carbon emissions by more than 15% without increasing the economic burden, which validates the effectiveness in improving the economic and low-carbon performance of MGCs' operation.

This article investigates the voltage restoration problem of direct current (DC) microgrids (MGs) under stealth attacks. The fact is that secondary control requires information exchange through sparse communication networks, which makes cybersecurity the key to achieving voltage restoration. Compared with the attack signals launched by nonintelligent attackers, stealthy attack signals are more difficult to capture. Similarly, since MGs actually operate in a closed environment, malicious attackers have limited knowledge of the MG’s structural information. Therefore, we reformulate the problem to a two-person zero-sum game between the attacker and defender and then seek the optimal voltage restoration control strategy. In the process of solving the optimal problem, both robustness and convergence of parameter estimation are considered in this article to enhance the feasibility of adaptive dynamic programming schemes. Additionally, while ensuring the voltage restoration performance of DC MGs and considering the constraints of actual network communication, a novel dynamic event-triggered mechanism is designed to reduce information transmission pressure. The effectiveness of the established control scheme is verified through real-time testing equipment built on OPAL-RT and comparison results.

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission.

The reliability of the microgrid (MG) can be improved by interconnecting MGs that are in close proximity, because the power shortfall in one MG can be compensated by the excess power available from other interconnected MGs. For multiple dc MG clusters consisting of a large number of heterogeneous distributed generators (DGs), this article establishes a master-slave cooperation framework containing a two-layer voltage estimator. All master-DGs implement current economical allocation among multiple MG clusters and drive their respective slave-DGs to realize current sharing accuracy. Compared with the previous work, the proposed control strategy has the advantages of simultaneous achievement of accurate current sharing and current economical allocation, short time consumption and faster convergence, and robustness against uncertain communication environments. Moreover, all distributed controllers are allowed to be implemented in a variable cyber network, thus well matching the characteristics of frequent switching operation in MG systems. Sufficient conditions in terms of control time constants for the two-layer cyber network are also deduced to ensure entire system stability. Different cases are tested to verify the effectiveness of the results in MATLAB/SimPowerSystems.

In actual microgrids (MGs) networks, the information exchange between distributed energy resources (DERs) agents may be subject to various types of measurement noises and effected by communication time delays. This article proposes a resilient distributed multiagent control scheme for ac MG networks subject to additive noise and time-delay disturbances. The proposed multiagent control scheme is composed of three distributed consensus protocols, which is able to synchronize the output voltages and frequencies of inverter-based DERs to their reference values and achieve the optimal active power-sharing property by a low bandwidth communication network with noise and time-delay disturbances in almost sure convergence. By means of the stochastic analysis tools and algebraic graph theory, distributed consensus control protocols are designed to be employed for the secondary control level of MGs. On this basis, we deduce the stability criteria of the closed-loop MG system under noise and time-delay disturbances. As a result, the proposed consensus protocols can well restore the voltage and frequency’s derivation produced at the primary control level, meanwhile, can well achieve the optimal power sharing even though there exist communication disturbances. Several simulation scenarios on an islanded MG network are provided to verify the proposed control protocols’ performance.

This article proposes a nonlinear distributed cooperative control scheme that can regulate the power output to achieve efficient utilization of renewable energy in ac microgirds, which ensures mean-square autonomous proportional power sharing over a nonlinear microgird system via a sparse cyber network subject to noisy disturbance and limited bandwidth constraints. The cyber networks are exposed to noisy disturbances and limited bandwidth constraints that terribly reduce the stability and quality of the whole system. To eliminate the adverse effects of noisy disturbances and limited bandwidth constraints, we propose a robust distributed control strategy designed by using partial feedback linearization for the dynamical nonlinear model of a microgrid system. Moreover, a distributed event detection mechanism with noise-dependent threshold is adopted to update the control signals with the consideration of unnecessary data communication reduction. Through adopting stochastic stability theory and Lyapunov function, the stability and convergence analysis of the proposed dynamic distributed event-detection conditions considering noise interferences is derived. As a result, the suggested method decreases the sensitivity of the system to failures and increases its reliability. Finally, a modified IEEE 34-bus test system in MATLAB/Simulink is utilized to verify the effectiveness of the proposed controller design scheme.

Reducing time consumption of economical power allocation operation among multiple microgrid (MG) clusters can significantly enhance the balance efficiency between power supply and load demand. In this article, a novel secondary power management strategy with cluster-oriented two-layer cooperative (TLC) framework is proposed, by which both the power sharing requirement for all DGs within each MG cluster and the economical power allocation demand among multiple MG clusters can be simultaneously realized during the secondary control process. In the framework, all cluster-head distributed generators (DGs) constitute the upper control layer, which enable the economical power allocation operation among multiple MG clusters, and all noncluster-head DGs constitute the lower control layer allowing the power sharing adjustment within each MG cluster. All the power mismatches across the TLC framework are fed back in the primary control to generate the frequency/voltage nominal set-points. Sufficient conditions, in terms of control time constants of the TLC framework and connectivity of the two-layer cyber network, are derived to guarantee the stability of the entire multiple MG cluster system with both power balance and power generation constraints. Specially, both the lower and upper layer controllers are designed based on a sparse two-layer cyber network, allowing different numbers of heterogeneous DGs in each MG cluster. The effectiveness of the control methodology is verified by the simulation of a multiple ac MG cluster system in MATLAB/SimPowerSystems.

In this paper, a robust neighbor-based distributed cooperative control strategy is proposed for dc cyber-physical microgrids, considering communication delays and slow switching topologies. The proposed robust control strategy can synchronize the voltages of a dc microgrid to the desired value while achieving the optimal load sharing for minimizing distributed energy resources' (DERs) generation cost to achieve their economic operation at the same layer via a sparse communication network considering communication delays and slow switching topologies synchronously. The continuous interaction of physical-electrical and cyber networks generally exacerbates the occurrence of communication delays. Moreover, the arbitrary switching topologies could destroy the system's transient characteristics at the switching time instants. To further quantify these impacts on the system stability, the communication delay and average switching dwell-time-dependent control conditions for the proposed control strategy are proved based on the Lyapunov-Krasovskii theory. Some sufficient conditions for the exponential stability of the cyber-physical delayed-switching system are developed, which guarantees the robustness of the proposed strategy against the communication delays and dynamically changing interaction topologies. The proposed control protocols are shown to be fully distributed and implemented through a sparse communication network. Finally, several cases on a modified IEEE 34-bus test network are investigated which demonstrate the effectiveness and performance of the results.

In this paper, a novel distributed pinning control scheme for the voltage regulation of active distributed networks through limited bandwidth communication. In actual active distributed networks, the information exchange among multiple and cooperative DGs may be subject to these constraints of communication bandwidth and storage space, DGs can only receive uniform quantized information. Towards that end, a distributed pinning control scheme will synchronize the terminal voltage of DGs to their reference value by a virtual leader through sparse communication considering communication band- width and storage space exponential convergence. By means of the Gronwall's inequality tools and algebraic graph theory, distributed pinning protocols are developed to be employed for active distributed networks, in which manner the criteria for the stability analysis to maintain the closed-loop system stable are derived. Simulation results on an active distributed network system are provided to show the effectiveness of the proposed control protocols.

This paper presents a robust distributed event-triggered control strategy that will regulate the power output of massive photovoltaic (PV) generators in a low-voltage (LV) microgird, which can achieve all of PV generators to operate at the same ratio of available power based on their status and capacity through a spare network with limited communication bandwidth and time delays. Due to employing event-triggered communication with time delays, the proposed control strategy is fully distributed and only driven at their own event time, which effectively reduces the frequency of controller updates compared with continuous-time feedback control, moreover is also robust to time delays. Furthermore, each PV only requires the local voltage and current measurement from its own and some nearest neighbors (but not all) for the distributed power control at the last event-triggered time to achieve the active and reactive power outputs to operate at the same ratio. The inequality technique is employed to devise the stability and convergence analysis of the proposed dynamic event-triggered conditions. The effectiveness of the proposed control strategy is verified under various scenarios by a modified IEEE 34-bus test network in MATLAB/SimPowerSystems.

Matching power transfer between microgrids (MGs) enables maximum utilization of distributed energy resources (DERs), this paper proposes a cluster-oriented cooperative control strategy for multiple ac MG clusters, under which the power sharing among multiple MG clusters can be achieved by an intercluster scheme, whereas the frequency/voltage of all DERs within each MG cluster can also be regulated by an intracluster scheme. By pinning one or some cluster-head DERs from each MG cluster to constitute an intercluster communication network, the intercluster control layer can generate the frequency/voltage references based on the power mismatch among multiple MG clusters. In the intracluster control layer, the pinned DERs propagate these references to their neighbors in an MG cluster, and the frequency/voltage nominal set-points for each DER in the primary control process can be adjusted based on the frequency/voltage errors across the intracluster communication networks. Since the evolutions of intra- and intercluster dynamics may involve different time scales, the upper bound for the ratio of the associated intra- and intercluster time constants is finally derived to guarantee the stability of the whole multi-MG-cluster system. In special, both the intra- and intercluster controllers are designed based on their own sparse cyber networks, allowing different numbers of heterogeneous DERs in each MG cluster. The effectiveness of the control methodology is verified by the simulation of an ac multi-MG-cluster system in MATLAB/SimPowerSystems.

This paper develops a novel distributed iterative event-triggered control scheme for a master-slave-organized dc microgrid network with limited communication bandwidth. The proposed scheme can synchronize the voltage of multiple distributed energy resources (DER) to their desired value. Moreover, the optimal load sharing for their economic operation (e.g., minimize the total generation cost) can be achieved through a low bandwidth communication network. The designed controllers are fully distributed and only triggered at their own event time, which effectively reduces the frequency of controller updates compared with continuous-time feedback control. Eventually, each DER only requires the local voltage and current measurement from its own and some nearest (but not all) neighbors at given event-triggered time through limited-bandwidth communication links. The Lyapunov technique is employed to derive the event-triggered conditions that guarantee the stability. Furthermore, the lower bound of the interevent intervals is captured by the proposed iterative algorithm to exclude Zeno behaviors. Different cases in MATLAB/SimPowerSystems are investigated and results demonstrate the effectiveness and the performance of the proposed approach.

This paper proposes a distributed hierarchical cooperative control strategy for a cluster of islanded microgrids (MGs) with intermittent communication, which can regulate the frequency/voltage of all distributed generators (DGs) within each MG as well as ensure the active/reactive power sharing among MGs. A droop-based distributed secondary control scheme and a distributed tertiary control scheme are presented based on the iterative learning mechanics, by which the control inputs are merely updated at the end of each round of iteration, and thus, each DG only needs to share information with its neighbors intermittently in a low-bandwidth communication manner. A two-layer sparse communication network is modeled by pinning one or some DGs (pinned DGs) from the lower network of each MG to constitute an upper network. Under this control framework, the tertiary level generates the frequency/voltage references based on the active/reactive power mismatch among MGs while the pinned DGs propagate these references to their neighbors in the secondary level, and the frequency/voltage nominal set points for each DG in the primary level can be finally adjusted based on the frequency/voltage errors. Stability analysis of the two-layer control system is given, and sufficient conditions on the upper bound of the sampling period ratio of the tertiary layer to the secondary layer are also derived. The proposed controllers are distributed, and thus, allow different numbers of heterogeneous DGs in each MG. The effectiveness of the proposed control methodology is verified by the simulation of an ac MG cluster in Simulink/SimPower Systems.

This paper develops a distributed cooperative control scheme for DC microgrids with time-varying coupling delays in noisy environments. The proposed distributed cooperative control, consisting of distributed primary control and secondary control, will regulate the voltage of microgrids to the desired values and through a sparse communication network with asymmetric communication delays and noise disturbances. Distributed cooperative controllers are designed into the secondary control stage for DC microgrids, in which manner the criteria for the stability analysis and delays boundedness to maintain the system stable are derived. With the proposed algorithms, control derivation of voltage produced during the primary control stage can be well remedied even if the communication delays and noise disturbances may exist. The effectiveness of the proposed control methodology is verified by the simulation of a DC microgrid system in MATLAB/SimPowerSystems.

This paper investigates the finite-time control for robust tracking consensus problems of multiagent systems with an uncertain leader for situations where the state of the considered active leader may not be measured and the directed network topology is time-varying. Based on the neighbor-based state-estimation rule and a new Lyapunov stability analysis method, a continuous and nonlinear distributed tracking protocol using only relative position information is designed, under which each agent can follow the leader in finite time if the input (acceleration) of the leader is known, and the tracking errors can converge to a bounded region in finite time if the input of the leader is unknown. In particular, a special continuous distributed tracking protocol with bounded control inputs is introduced to track the active leader in finite time. Numerical simulations are also given to illustrate the effectiveness of the theoretic results.

This paper presents a robust distributed secondary control (DSC) scheme for inverter-based microgrids (MGs) in a distribution sparse network with uncertain communication links. By using the iterative learning mechanics, two discrete-time DSC controllers are designed, which enable all the distributed energy resources (DERs) in an MG to achieve the voltage/frequency restoration and active power sharing accuracy, respectively. In special, the secondary control inputs are merely updated at the end of each round of iteration, and thus, each DER only needs to share information with its neighbors intermittently in a low-bandwidth communication manner. This way, the communication costs are greatly reduced, and some sufficient conditions on the system stability and robustness to the uncertainties are also derived by using the tools of Lyapunov stability theory, algebraic graph theory, and matrix inequality theory. The proposed controllers are implemented on local DERs, and thus, no central controller is required. Moreover, the desired control objective can also be guaranteed even if all DERs are subject to internal uncertainties and external noises including initial voltage and/or frequency resetting errors and measurement disturbances, which then improves the system reliability and robustness. The effectiveness of the proposed DSC scheme is verified by the simulation of an islanded MG in MATLAB/SimPowerSystems.