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Suppose in a given planar circular region, there are smart mobile evaders and we want to find them using sweeping agents. We assume the sweeping agents are in a line formation whose total length is predetermined. We propose procedures for designing a sweeping process that ensures the successful completion of the task, thereby deriving conditions on the sweeping velocity of the linear formation and its path. Successful completion of the task means that evaders with a given limit on their velocity cannot escape the sweeping agents. We present results on the search time given the initial conditions.

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual agents. However, in high‐dimensional continuous spaces, the non‐stationary environment can provide outdated experiences that hinder convergence, resulting in ineffective training performance for multi‐agent systems. To tackle this issue, a novel reinforcement learning scheme, Mutual Information Oriented Deep Skill Chaining (MioDSC), is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration efficiency. These rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment state. In addition, MioDSC can generate cooperative policies using the options framework, allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent learning. MioDSC was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty levels. The experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

This paper addresses the difficulties with connected vehicle systems (CVSs), particularly with vehicle platooning, are examined in this paper. For leader and follower-connected vehicles, the control protocol (which includes artificial delays, disturbances and proportional gains) is implemented. With tracking error systems, system dynamics are modelled while taking outside influences into consideration. Using creative thinking, a centralized event-triggered control system is implemented to maximize fleet wide communication updates. System stability is guaranteed by this centralized method in combination with quadratic form Lyapunov stability analysis. The risk of zeno behaviour is reduced by an event-triggered communication condition that is activated when a threshold is exceeded. The effectiveness of the centralized event-triggered system in improving stability and resilience in connected vehicle platooning scenarios is evaluated numerically through simulations.

This paper mainly focuses on the fixed-time consensus (FXC) control problem for nonlinear multi-agent systems (MASs). For the cases of leader-following and leaderless, two adaptive discontinuous protocols are designed, respectively, to realize our control goals. Common adaptive control protocols always significantly increase the dimension of the considered system model, while the protocols presented here only require two adaptive update laws and are therefore simpler to apply in the engineering control. Moreover, no additional conditions are required to ensure that the system can achieve FXC successfully, except for some necessary assumptions. Simulation examples also illustrate that these two protocols are effective.

This paper aims to solve the consensus problem of three different types of multi-agent systems under fixed communication delay. For these three different types of multi-agent systems, three new control protocols are constructed to solve the consensus issue of multi-agent systems with coupling weights, which is rarely considered in other related articles. For fixed coupled multi-agent systems, a new control strategy is designed by Lyapunov theory, matrix theory, and the inequality method to ensure the consensus of multi-agent systems. An adaptive coupling weight updating scheme is proposed for adaptive coupling multi-agent systems to achieve the consensus state of the multi-agent systems. Finally, experimental results show the effectiveness of the proposed three different algorithms.

In multi-agent reinforcement learning (MARL), the large number of agents can lead to information overload, hindering effective learning in large-scale systems such as industrial control and smart manufacturing environments. While mean field methods offer scalable solutions in homogeneous agent environments, real-world scenarios often involve heterogeneous agent decision making and dynamic interaction structures. To address these challenges, attention-based adaptive mean field methods have emerged. However, they still face limitations: (1) neighbor-weighted mean field lacks a global perspective, limiting the ability to model global coordination in systems; (2) single-scale mean field representations struggle to capture multi-level agent interactions critical for scene interaction optimization. To overcome these limitations, we propose a multi-scale mean field reinforcement learning framework, integrating far-field global action distributions with near-field local interactions weighted by attention. By leveraging multi-head attention, our method comprehensively captures interactions at different scales, enabling more adaptive decision making. Experimental results demonstrate superior performance and scalability across various benchmark tasks, highlighting its potential for enhancing decision-making in complex environments.

Multi-agent systems constitute a wide class of decentralised systems. Their functions are usually carried out by collaborative activities of agents. To ensure resilience of multi-agent systems, we should endow them with a capability to dynamically reconfigure. Usually, as a result of reconfiguration, the existing relationships between agents are changed and new collaborations are established. This is a complex and error-prone process, which can be facilitated by the use of formal reasoning and automated verification. In this paper, we propose a generic resilience-explicit formalisation of the main concepts of multi-agent systems. Based on it, we introduce corresponding specification and refinement patterns in Event-B. Our patterns facilitate modelling behaviour of resilient multi-agent systems in a rigorous systematic way and verification of their properties. We demonstrate the application of the proposed approach by a case study—a smart warehouse system.

Increasing the load demand and penetration of renewable energy sources (RESs) poses real challenges for optimal energy management of distribution networks. Moreover, considering multi-carrier energy systems has increased the efficiency of systems, and provides an opportunity for using the advantages of RESs. In this regard, we adopted a new framework based on the new challenges in the multi-carrier energy micro-grid (MEMG). In the proposed method, a comprehensive MEMG was modeled that benefits from a large assortment of distributed energy resources (DERs), such as micro-turbines, fuel cells, wind turbines, and energy storage. Considering many DERs is necessary, because these resources could cover one another’s disadvantages, which have a great impact on the total cost of the MEMG and decrease the emission impacts of fossil-fuel-based units. Furthermore, waste power plants, inverters, rectifiers, and emission constraints are considered in the proposed method for modeling a practical MEMG. Additionally, for modeling the uncertainty of stochastic parameters, a model based on a multilayer neural network was used in this paper. The results of this study indicate that using a decentralized model, along with stochastic methods for predicting uncertainty, can reduce operational costs in micro-grids and computational complexity compared with optimal centralized programming methods. Finally, the equations and results obtained from the proposed method were evaluated by experiments.

The authors propose a self‐triggered synchronisation control method of a general high‐order linear time‐invariant multi‐agent system through a cloud repository. In the cloud‐mediated self‐triggered control, each agent asynchronously accesses the cloud repository to obtain past information about its neighbouring agents. Then, the agent predicts future behaviours of its neighbours as well as of its own and locally determines its next access time to the cloud repository. In the case of a general high‐order linear agent dynamics, each agent has to estimate exponential evolution of its trajectory characterised by eigenvalues of a system matrix, which is different from single/double integrator or first‐order linear agents. The authors’ proposed method deals with exponential behaviours of the agents by tightly evaluating the bounds on matrix exponentials. Based on these bounds, the authors design the self‐triggered controller through a cloud that achieves the bounded state synchronization of the closed‐loop system without exhibiting any Zeno behaviours. The effectiveness of the proposed method is demonstrated through the numerical simulation. The authors propose a self‐triggered synchronisation control method of a general linear time‐invariant multi‐agent system through a cloud repository. Under asynchronous communication through a cloud, each agent has to handle uncertainties on the behaviours of its neighbouring agents as well as itself. Our proposed method deals with exponential behaviours of the agents by tightly evaluating the bounds on matrix exponentials and achieves the bounded state synchronization of the closed‐loop system without exhibiting any Zeno behaviours.

Microgrids have emerged to diversify conventional electric generation using small‐scale distributed generation. Large efforts have been put into designing control strategies to optimise the power schedules of microgrids, however, verification that such control systems also are reliable in terms of stability during normal operation and fault conditions is needed. This study presents a hierarchical distributed control system that fulfils these conditions for an AC microgrid. The stability maintained by proposed controller, considering the large signal model, is analysed with the use of Lyapunov's direct method. Resilient control distribution is achieved by the implementation of suitable forecast models and fault‐tolerance mechanisms to avoid single points of failure. The resilience of the control system is verified with the use of graph theory. The stable and resilient operation of the proposed control system is tested by a real‐time microgrid model implemented with an OPAL‐RT real‐time simulator, combined with a communication network built with Raspberry Pis, testing the control system presented under normal and faulty conditions. Simulation results show a stable operation in terms of voltage and frequency in both conditions, resilient operation is shown for the faulty condition case. Additionally, cost minimisation performance is included to validate optimal power management capabilities. The future energy systems are required to combat climate change. To incentive its development it is necessary to demonstrate that the new technologies are reliable, economically and technically viable. The use of Lyapunov methods and graph theory are used to guarantee reliability and stability of the proposed distributed control system for cost minimisation for microgrids in real‐time simulation.