Abstracts:The growing demand for microgrids (MGs) and networked microgrids (NMGs) is driven by the prospects of improved reliability of energy supply and enhanced resilience of the energy grid in changing energy demands. The integration of data communications and industrial control systems has enabled NMGs to provide real-time monitoring and refined control for applications such as energy load balancing. These systems rely on current public-key cryptosystems to secure data communications based on certain computational hardness assumptions. However, the advent of quantum computing poses a significant threat to these cryptosystems because quantum algorithms, such as Shor’s algorithm, have demonstrated that quantum computers are capable of cracking these cryptosystems in practical time. To address this emerging challenge, quantum secure technologies such as postquantum cryptography and quantum key distribution are being explored as complementary approaches to achieve quantum-resilient security. This article reviews the ideas of quantum-secured NMGs (QS-NMGs) and identifies the relevant quantum-cryptographic technologies for QS-NMGs. The study will help scientists, engineers, and policymakers identify limitations of the current literature and discover future research avenues.

Abstracts:This article gives an investigation to the leader-following consensus issue for Lipschitz nonlinear multiagent systems. A double dynamic event-triggered control scheme has been proposed to reduce unnecessary triggering behaviors and thus save resources. This scheme consists of two independent dynamic event triggering mechanisms: one controls communication between agents, and the other determines controller updates. To avoid Zeno behavior, a constant has been added to the triggering mechanism. Moreover, a simplified parameter selection procedure is proposed that eliminates the need for verification of matrix inequalities. In addition, this work was explored in a directed network consisting of only one spanning tree. Finally, the correctness of the presented control strategy is verified through a simulation example.

Abstracts:Existing distributed optimal control methods for multiagent systems (MASs) are predominantly confined to low-order dynamics. Moreover, these methods often fail to address the coupling effects of communication delays and external disturbances. Typically, they rely on predictive mechanisms that require substantial communication overhead. This study focuses on the distributed optimal consensus control problem of high-order nonlinear MASs with communication delays and disturbances, aiming to overcome the limitations of existing approaches. First, a PI variable incorporating communication delays is formulated using passive scattering transformation. Second, we achieve distributed optimization control for MASs with communication delays by applying scattering transformation techniques. Furthermore, this article proposes a PI regulator that integrates with Nussbaum functions to achieve optimal consensus for high-order nonlinear agents with unknown control directions and bounded external disturbances. Finally, numerical simulations demonstrate the efficacy of our distributed optimization algorithm.

Abstracts:This article studies the leader–follower output consensus problem for heterogeneous nonlinear multiagent systems under event-triggered communication. To estimate the leader’s output, an estimator incorporating a static gain is first designed for each agent, ensuring that the estimation error converges to a prespecified accuracy in finite time. Based on the gain-based estimator, an adaptive control scheme is proposed, which not only achieves global boundedness of all closed-loop signals, but also guarantees that the consensus error converges to an arbitrarily small neighborhood of the origin in finite time. Distinct from related results, the agents in this article exhibit both different dynamics and state dimensions. Moreover, asynchronous and discontinuous communication among neighboring agents is achieved, and the nondifferentiability problem arising from the use of triggered states is effectively avoided. A simulation example is given to demonstrate the main results.

Abstracts:This article concentrates on the active-constrained consensus problem for high-order multiagent systems with nonuniform position constraints, switching graphs, and communication delays. The main challenge lies in overcoming the strong nonlinearities introduced by the coupling between the position constraints and the integral relationships of the high-order agents’ states. To tackle this issue, we propose an active-constrained consensus algorithm and establish a constrained equivalent system to keep agents within their respective position constraint sets. Through a series of model transformations and fully exploiting the convexity of the equivalent system, it is demonstrated that consensus can be achieved even when position constraints, high-order dynamics, switching graphs, and communication delays coexist. Finally, simulation results are included to verify the conclusions.

Abstracts:This article proposes a technique to improve the performance of power systems that are subjected to random topology changes and communication-related latencies. A Markov jump system framework is established to model abrupt mode transitions caused by stochastic events, such as line faults and lightning strikes in single-machine infinite bus systems. Considering practical delays between supervisory control and data acquisition-based mode detection and control implementation, an asynchronous control architecture is developed in which the controller runs under mismatched mode information. A data-driven Q-learning-based policy iteration algorithm is proposed to compute the optimal controller gains without requiring knowledge of system dynamics or mode transition probabilities. The algorithm relies solely on previously collected input-state data that satisfy a rank condition and converges to the model-based optimal solution. Simulation studies show that the proposed controller reduces transient state deviations by about 30% compared with an linear matrix inequalities-based asynchronous controller, ensuring stability and effective damping performance under both mode mismatch and system uncertainties.

Abstracts:Distributed consensus control, with its advantages of higher efficiency and better accuracy, has gradually replaced centralized control and become a common method to regulate voltage and share current in DC microgrids. However, the false data injection attacks targeting communication channels bring great security risks to DC microgrids, and existing resilient control methods are still plagued by their poor applicability and high implementation costs. To this end, this article proposes an event-triggered communication-based consensus control scheme, which consists of a decentralized fault-tolerant controller using input–output feedback linearization and a distributed attack observer based on the dual-layer cyber network with the event-triggered communication strategy. Lyapunov analysis method proves that the proposed scheme can effectively resist the unbounded false data injection attacks and guarantee the resilient control of bus voltage regulation and current sharing for DC microgrids. Finally, a DC microgrid model is built in the hardware-in-the-loop testbed to validate the effectiveness and superiority of the proposed scheme.

Abstracts:The resilience of uncrewed aerial vehicle (UAV) swarms is critical for ensuring system performance in challenging environments. Existing resilience research predominantly employs binary failure models, which overlook the residual capabilities of UAVs following partial payload failure and consequently limit the effectiveness of recovery strategies. To address this gap, this article proposes a payload-level swarm resilience enhancement framework. First, a payload-oriented state model is constructed, correlating swarm performance with the functional status of payloads. Second, a bidirectional task reallocation (BDTR) method is introduced as a proactive enhancement mechanism. This method adopts a dynamic “task offloading followed by task acquisition” interaction to fully leverage the swarm's residual capabilities and achieve load balancing. Simulation results demonstrate that BDTR significantly outperforms benchmark methods in comprehensive resilience while maintaining the highest mission completion rate. Validation through Monte Carlo simulations shows an average mission completion rate of 0.829 and an average resilience index of 0.862, mitigating the tradeoff of “high completion rates with poor execution efficiency” observed in conventional baseline methods. This study offers a new theoretical foundation and practical pathway for enhancing UAV swarm resilience, supporting the development of more reliable autonomous systems.

Abstracts:This article presents a novel decentralized energy market framework developed for multienergy hybrid energy storage systems, incorporating battery energy storage, fuel cells with thermal and hydrogen storage, and prosumers as autonomous market participants. The key contributions are threefold: first, the strategic deployment of hybrid energy storage systems as multifunctional assets supporting energy arbitrage, combined heat and power generation, and sector coupling, second, the development of a fully decentralized peer-to-peer trading platform using the alternating direction method of multipliers, which ensures efficient coordination while preserving data privacy, and third, a dynamic energy sourcing mechanism that enables prosumers to optimize electricity and gas usage based on real-time market signals. Numerical simulations on a representative test network demonstrate the effectiveness and robustness of the proposed approach, achieving near-optimal performance with only a 0.59% deviation from the centralized benchmark while maintaining complete privacy.

Abstracts:In swarm drone applications, the problem of multitarget self-organizing search in unknown environments has demonstrated immense potential. In this problem, agents need to collaboratively search for multiple dynamic targets while only observing their immediate surroundings. However, the computational complexity and uncertainty in traditional multiagent search processes have been critical factors limiting their performance. Therefore, this article focuses on the multitarget search problem. First, a multiagent search environment including agents, targets, and obstacles is constructed. Subsequently, this article proposes a multiagent multitarget search method (MASOS) in complex obstacle environments. This method integrates the Actor–Critic reinforcement learning algorithm and multi-head attention for UAV swarm collaborative control, enabling agents to focus more on critical information such as other agents, targets, and obstacles during observation. It aims to enhance search, collaboration, and obstacle avoidance capabilities in multitarget self-organizing search tasks. Experimental results show that the MASOS method outperforms other commonly used multiagent reinforcement learning algorithms in terms of coordination strategies. In large-scale self-organizing search tasks, the capture success rate of the MASOS method is nearly 100%. Finally, By designing multitask scenario experiments, conducting comparative analyses combining multilayer perceptron and multi-head attention mechanism, and performing ablation studies on key modules, the effectiveness and interpretability of the MASOS method are verified from two aspects: Performance and decision-making logic.