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Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost-effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi-dimensional interests of different agents in the multi-energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi-agent game. Firstly, the multi-energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi-agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi-energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q-learning and the artificial intelligence method of Petri-net. Finally, the learning and decision-making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.

The emergence of the graphical characterization of multiagent controllability has raised several issues concerning how to directly establish controllability from topology structures. Arguably, one of the most serious challenges to this research field is the means through which equivalent partition, which plays an important role in graphical characterization, obtains sufficient controllability conditions; hence, how equivalent partition influences controllability has garnered considerable attention. This article specifically focuses on the sufficient conditions and limitations of equivalent partition in multiagent controllability. We provide two sufficient conditions: (i) the absence of the system matrix’s eigenvectors that make the equation formed by the eigenvalues and eigenvectors hold and (ii) the addition of leaders by reducing the same number of followers. The first condition particularly exhibits a relation between two apparently unrelated parts: Tao’s equation and controllability. We further propose a necessary and sufficient condition for controllability under n -node graphs ( n ⩽ 5) by taking advantage of iso-neighbor nodes, and analyze the resulting difficulties when n is greater than 5. Immediate corollaries of our results are obtained. Finally, we reveal the limitation of equivalent partition in controllability analysis. Several constructive examples demonstrate our results.

We introduce a strategy matrix, a novel concept for ensuring controllability in game-based control systems (GBCSs). This graph-based condition is presented as an alternative to utilizing complex mathematical calculations through algebraic conditions. Moreover, to address these issues, one must first study the expression of Nash equilibrium actions. This expression yields a general formula of the game controllability matrix, which is always affected by the specific matrix (strategy matrix) comprising Nash equilibrium actions, and the matrix can not only be obtained by matrix calculation but can also be directly written through the topology, indicating the topology’s specific influence on the GBCS. Finally, we build a new game-based multi-agent system and determine the controllability relationship between the system and the general system.

This paper presents a new decentralized algorithm for the deployment and reconfiguration of a multi-agent formation in a convex bounded polygonal area when considering several outgoing agents. The system is deployed over a two-dimensional convex bounded area, each agent being driven by its own linear model predictive controller. At each time instant, the area is partitioned into Voronoi cells associated with each agent. Due to the movement of the agents, this partition is time-varying. The objective of the proposed algorithm is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. When some agents present a non-cooperating behavior (e.g. agents required for a different mission, faulty agents, etc.), they have to leave the formation by tracking a reference outside the system’s workspace. The outgoing agents and their objective positions partition the convex bounded polygonal area into working regions. Each remaining agent will track a new objective point allowing it to avoid the trajectory of the outgoing agents. The computation of this objective position is based on the agent’s safety region (i.e. the intersection of the contracted Voronoi cell and the contracted working region). When the outgoing agents have left the workspace, the remaining agents resume their deployment objective. Simulation results on a formation of a team of unmanned aerial vehicles are finally presented to validate the algorithm proposed in this paper when several agents leave the formation.

Sustainable cities are envisioned to have economic and industrial steps toward reducing pollution. Many real-world applications such as autonomous vehicles, transportation, traffic signals, and industrial automation can now be trained using deep reinforcement learning (DRL) techniques. These applications are designed to take benefit of DRL in order to improve the monitoring as well as measurements in industrial internet of things for automation identification system. The complexity of these environments means that it is more appropriate to use multi-agent systems rather than a single-agent. However, in non-stationary environments multi-agent systems can suffer from increased number of observations, limiting the scalability of algorithms. This study proposes a model to tackle the problem of scalability in DRL algorithms in transportation domain. A partition-based approach is used in the proposed model to reduce the complexity of the environment. This partition-based approach helps agents to stay in their working area. This reduces the complexity of the learning environment and the number of observations for each agent. The proposed model uses generative adversarial imitation learning and behavior cloning, combined with a proximal policy optimization algorithm, for training multiple agents in a dynamic environment. We present a comparison of PPO, soft actor-critic, and our model in reward gathering. Our simulation results show that our model outperforms SAC and PPO in cumulative reward gathering and dramatically improved training multiple agents.

In the recent paper (Monshizadeh et al. in IEEE Trans Control Netw Syst 1(2):145–154, 2014. https://doi.org/10.1109/TCNS.2014.2311883), model reduction of leader–follower multi-agent networks by clustering was studied. For such multi-agent networks, a reduced order network is obtained by partitioning the set of nodes in the graph into disjoint sets, called clusters, and associating with each cluster a single, new, node in a reduced network graph. In Monshizadeh et al. (2014), this method was studied for the special case that the agents have single integrator dynamics. For a special class of graph partitions, called almost equitable partitions, an explicit formula was derived for the H2 model reduction error. In the present paper, we will extend and generalize the results from Monshizadeh et al. (2014) in a number of directions. Firstly, we will establish an a priori upper bound for the H2 model reduction error in case that the agent dynamics is an arbitrary multivariable input–state–output system. Secondly, for the single integrator case, we will derive an explicit formula for the H∞ model reduction error. Thirdly, we will prove an a priori upper bound for the H∞ model reduction error in case that the agent dynamics is a symmetric multivariable input–state–output system. Finally, we will consider the problem of obtaining a priori upper bounds if we cluster using arbitrary, possibly non almost equitable, partitions.

The share and variants of coupling points (CPs) between different energy carrier networks (such as the gas or power grids) are increasing, which results in the necessity of the analysis of so-called multi-energy systems (MES). One approach is to consider the MES as a graph network, in which coupling points are modeled as edges with energy efficiency as weight. On such a network, local coalitions can be formed using multi-agent systems leading to a dynamic graph partitioning, which can be a prerequisite for the efficient decentralized system operation. However, the graph can not be considered static, as the energy units representing CPs can shut down, leading to network decoupling and affecting graph partitions. This paper aims to evaluate the effect of network adaptivity on the dynamics of an exemplary coalition formation approach from a complex network point of view using a case study of a benchmark power network extended to an MES. This study shows: first, the feasibility of complex network modeling of MES as a cyber-physical system; second, how the coalition formation system behaves, how the coupling points impact this system, and how these impact metrics relate to the CP node attributes.

Mathematical modelling is an important aspect in apprehending discrete and continuous physical systems. Multipolar uncertainty in data and information incorporates a significant role in various abstract and applied mathematical modelling and decision analysis. Graphical and algebraic models can be studied more precisely when multiple linguistic properties are dealt with, emphasizing the need for a multi-index, multi-object, multi-agent, multi-attribute and multi-polar mathematical approach. An m-polar fuzzy set is introduced to overcome the limitations entailed in single-valued and two-valued uncertainty. Our aim in this research study is to apply the powerful methodology of m-polar fuzzy sets to generalize the theory of matroids. We introduce the notion of m-polar fuzzy matroids and investigate certain properties of various types of m-polar fuzzy matroids. Moreover, we apply the notion of the m-polar fuzzy matroid to graph theory and linear algebra. We present m-polar fuzzy circuits, closures of m-polar fuzzy matroids and put special emphasis on m-polar fuzzy rank functions. Finally, we also describe certain applications of m-polar fuzzy matroids in decision support systems, ordering of machines and network analysis.

Mechatronic design optimization is a complex process characterized by an important number of requirements, design variables, constraints and objectives. Therefore, it is very important to decompose efficiently the system design problem into a set of partitions to minimize the computational cost while profiting from the spatial distribution of design tools, working teams and expertise. However, the optimization of the overall design requires incorporating the relevant partitions in order to find the optimum mechatronic design. Efficient strategies of partitioning and coordination should be specified at the conceptual level to have a successful optimization process. In this paper, a new approach based on multi-agent paradigm is proposed for mechatronic design optimization. The proposed approach is applied to the preliminary design case of an electric vehicle to demonstrate its validity and effectiveness.

Efficient data transmission and reasonable task allocation are important to improve multi-robot exploration efficiency. However, most communication data types typically contain redundant information and thus require massive communication volume. Moreover, exploration-oriented task allocation is far from trivial and becomes even more challenging for resource-limited unmanned aerial vehicles (UAVs). In this paper, we propose a fast and communication-efficient multi-UAV exploration method for exploring large environments. We first design a multi-robot dynamic topological graph (MR-DTG) consisting of nodes representing the explored and exploring regions and edges connecting nodes. Supported by MR-DTG, our method achieves efficient communication by only transferring the necessary information required by exploration planning. To further improve the exploration efficiency, a hierarchical multi-UAV exploration method is devised using MR-DTG. Specifically, the \\emph{graph Voronoi partition} is used to allocate MR-DTG's nodes to the closest UAVs, considering the actual motion cost, thus achieving reasonable task allocation. To our knowledge, this is the first work to address multi-UAV exploration using \\emph{graph Voronoi partition}. The proposed method is compared with a state-of-the-art method in simulations. The results show that the proposed method is able to reduce the exploration time and communication volume by up to 38.3\\% and 95.5\\%, respectively. Finally, the effectiveness of our method is validated in the real-world experiment with 6 UAVs. We will release the source code to benefit the community.