Survey of containment control in multi-agent systems: concepts, communication, dynamics, and controller design

Containment control of heterogeneous multi-agent systems

Sampled-data based containment control of continuous-time multi-agent systems with switching topology and time-delays

This paper considers the containment control of linear multi-agent system with weakly connected information topology. By defining the node-induced strongly connected subgraph as strong component, the agents contained in strong components with zero in-degree are called leaders and the others are called followers. Then, a sufficient and necessary condition is proposed for all agents to achieve containment by employing an observer-based protocol, where all followers finally converge to the convex hull formed by leaders and each strong component of leaders asymptotically achieve consensus. Since the number of leader agents in each strong component are always larger than one, the proposed scheme is more robust for the malfunction of leaders. Further, two LMIs are given to design the observer-based protocol. Numerical examples are included to illustrate the validity of the proposed results.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

Solving complex but structured problems in a decentralized manner via multiagent collaboration has received much attention in recent years. This is natural, as on one hand, multiagent systems usually possess a structure that determines the allowable interactions among the agents; and on the other hand, the single most pressing need in a cooperative multiagent system is to coordinate the local policies of autonomous agents with restricted capabilities to serve a system-wide goal. The presence of uncertainty makes this even more challenging, as the agents face the additional need to learn the unknown environment parameters while forming (and following) local policies in an online fashion. In this paper, we provide the first Bayesian reinforcement learning (BRL) approach for distributed coordination and learning in a cooperative multiagent system by devising two solutions to this type of problem. More specifically, we show how the Value of Perfect Information (VPI) can be used to perform efficient decentralised exploration in both model-based and model-free BRL, and in the latter case, provide a closed form solution for VPI, correcting a decade old result by Dearden, Friedman and Russell. To evaluate these solutions, we present experimental results comparing their relative merits, and demonstrate empirically that both solutions outperform an existing multiagent learning method, representative of the state-of-the-art.

This paper studies the containment control issue of multi-agent systems with time delay. By adopting continuous-time and sampled-data-based control protocols, we establish some necessary and sufficient containment control criteria for multi-agent systems with time delay. For the continuous-time case, our results show that both topology structure and time delay play an important role in the containment control of multi-agent systems, no matter whether the leaders are stationary (first-order system) or dynamic with constant velocity and time-varying position (second-order system). Furthermore, the maximum value of time delay to guarantee the achievement of containment control is given by the Hopf bifurcation theory. Moreover, we extend the results to the sampled-data case. Finally, numerical simulations are given to illustrate our main results.

In this study, bipartite containment control for multiagent systems (MASs) with quantitative information is investigated. Communication topology is structurally balanced, and the follower’s trajectory is within the area surrounded by the leader by designing distributed error terms. The leader is an external system, and the matrix information of the leader cannot be accessed by followers. Based on an adaptive quantization information distributed observer, the authors introduce an output feedback protocol to study bipartite containment control. Finally, simulations demonstrate the effectiveness of proposed control algorithms.

Sampled-data leader-following consensus of nonlinear multi-agent systems subject to impulsive perturbations

We investigate the fixed-time containment control (FCC) problem of multi-agent systems (MASs) under discontinuous communication. A saturation function is used in the controller to achieve the containment control in MASs. One difference from using a symbolic function is that it avoids the differential calculation process for discontinuous functions, which further ensures the continuity of the control input. Considering the discontinuous communication, a dynamic variable is constructed, which is always non-negative between any two communications of the agent. Based on the designed variable, the dynamic event-triggered algorithm is proposed to achieve FCC, which can effectively reduce controller updating. In addition, we further design a new event-triggered algorithm to achieve FCC, called the team-trigger mechanism, which combines the self-triggering technique with the proposed dynamic event trigger mechanism. It has faster convergence than the proposed dynamic event triggering technique and achieves the tradeoff between communication cost, convergence time and number of triggers in MASs. Finally, Zeno behavior is excluded and the validity of the proposed theory is confirmed by simulation.

The present paper proposes a multi-agent control system for rail transit. The multi-agent system (MAS) consists of multiple train agents, station agents and a central agent. The implementation of the proposed system is based on distributed optimal control. In the proposed system, each train agent can directly obtain information from the neighboring train agents. Each train agent consists of five subsystems, which are train data set, safety inspection system, timetable inspection system, energy optimization system and trajectory generate system. The train agent can optimize the speed trajectory according to the running state of adjacent trains with the cooperation of the five subsystems. The built-in algorithm of the proposed system can infer the driving strategy (such as time-priority or distance-priority) that should be adopted based on specific situations, which provides a good anti-disturbance ability. Furthermore, the distributed optimization method enables the system to perform a multi-objective optimization in a short time when the system is disturbed.

In recent years, Multi-agent systems (MAS) have shown their relevance to the design of distributed applications (logically or physically), complex and robust. The concept of agent is now more than an effective technology, it represents a new paradigm for software development in which the agent is autonomous software that has a goal, and operates in an environment and interacts with other agents using languages and protocols. With the progress of E-learning systems, the use of MAS in education field is essential, in fact, we propose to use MAS in virtual labs as a service in Cloud Computing that aims to resolve the synchronization troubles through interactions while using the same resource. In this article, we propose an architecture of virtual labs based on cloud computing and using Multi-Agents Systems. The introduction provides a generalized background of virtual labs based on cloud computing and MAS, then we highlight, on the one hand, the limits of classic labs and the importance of applying cloud computing in virtual labs. And on the other hand, the big interest of using Multi-Agent Systems (MAS) for best managing and provisioning shared infrastructure resources in Cloud Computing. Finally, we propose an abstract architecture divided into four layers and composed of seven agents and its specifications, with an application of a virtual lab in mechanic field.

Bipartite time-varying formation group containment control for multi-agent systems based on multi-layer network and semi-signed directed graph

Multiagent systems (MASs) can be exploited for malicious activities, which pose significant threats to public safety and national security. While current literature has explored countermeasures for individual or several agents, these approaches are inadequate for large-scale malicious MASs due to the lack of a systematic, global perspective. Additionally, the heterogeneity of MASs, wherein agents exhibit varying system weights, necessitates a strategy that prioritizes agents with high system weight to maximize disruption. To address these challenges, a consensus breakdown algorithm based on critical node identification and network topology decomposition is proposed. The proposed algorithm decomposes the network topology of both large-scale homogeneous and heterogeneous MASs by disabling critical nodes, thereby splitting MASs into multiple smaller agent clusters and preventing MASs from achieving global consensus. In scenarios involving both homogeneous and heterogeneous MASs, this approach transforms the critical node identification problem into a node regression problem. The algorithm leverages GraphSAGE, a highly efficient graph neural network (GNN) with a sampling mechanism, making it well-suited for feature extraction in large-scale networks while addressing potential computational constraints common in real-world applications. Relying on the sampling mechanism, GraphSAGE enhances computational efficiency when processing large-scale network topologies. To better fit the need for consensus breakdown, the information dissemination capabilities of nodes are considered when defining node importance. Furthermore, to extend the algorithm to the scenarios of heterogeneous MASs where agents have different system weights, the node importance is combined with the system weight of each agent to determine the final node criticality. Extensive simulation results validate the superior performance of the proposed algorithm across various aspects. Comparative experiments further demonstrate the accuracy and efficiency of the algorithm.

Multi-agent systems (MAS) have been an area of high expectations of the industrial IT community. However, in reality, these expectations are still not met and, in practice, the industry very rarely uses the MAS design methodologies, technologies, and software tools despite the appearance of many new classes of applications for which the MAS paradigm could be the perfect match. This paper analyzes the barriers and trends of the mismatch between the recent industrial anticipations and the real state of the practical use of MAS. It identifies engineering problems with very little re-use of code that currently stops economics of scale and impedes the extensive industrial MAS deployment and the ways to overcome them.

Analysing the synergies between Multi-agent Systems and Digital Twins: A systematic literature review