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The emerging digitalization in today's industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system's decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing secure control for networked multi-agent systems (MASs) under Denial-of-Service (DoS) attacks. We propose a constructive design method based on the interaction topology. The MAS with a non-attack communication topology, modeled by quasi-Abelian Cayley graphs subject to DoS attacks, can be represented as a switched system. Using switching theory, we provide easily implementable sufficient conditions for the networked MAS to remain asymptotically stable despite DoS attacks. Our results are applicable to both continuous-time and discrete-time systems, as well as to discrete-time systems with variable steps or systems that combine discrete and continuous times. [ABSTRACT FROM AUTHOR]

Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commissioning task involves finding specified items in a warehouse and moving them to a specified location using robots. This task is defined as a spatial task-allocation problem (SPATAP) based on a Markov decision process (MDP). It is considered a decentralized multi-agent system rather than a system that manages and optimizes agents in a central manner. Existing research on SPATAP involving modeling the environment as a MDP and applying Monte Carlo tree searches has shown that this approach is efficient. However, there has not been sufficient research into scenarios in which all agents are provided a common plan despite the fact that their actions are decided independently. Thus, previous studies have not considered cooperative robot behaviors with different goals, and the problem where each robot has different goals has not been studied extensively. In terms of the cooperative element, the item exchange approach has not been considered effectively in previous studies. Therefore, in this paper, we focus on the problem of each robot being assigned a different task to optimize the percentage of picking and delivering items in time in social situations. We propose an action-planning method based on the Monte Carlo tree search and an item-exchange method between agents. We also generate a simulator to evaluate the proposed methods. The results of simulations demonstrate that the achievement rate is improved in small- and medium-sized warehouses. However, the achievement rate did not improve in large warehouses because the average distance from the depot to the items increased. [ABSTRACT FROM AUTHOR]

In this paper, the problem of practical finite-time coordinated path following control of a multi-agent system in an uncertain Euler flow field is studied. The coordinated path following control system is decoupled into a path following sub-system and a coordinated formation sub-system. The uncertain Euler flow field is estimated by constructing an extended state observer. By embedding the extended state observer into the design of the finite-time controllers for both sub-systems, practical finite-time stability of the coordinated path following control system is proved. Simulation results are provided for validating the feasibility and effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Although the formation control of multi-agent systems has been widely investigated from various aspects, the problem is still not well resolved, especially for the case of distributed output-feedback formation controller design without input information exchange among neighboring agents. Using relative output information, this paper presents a novel distributed reduced-order estimation of the formation error at a predefined time. Based on the proposed distributed observer, a neural-network-based formation controller is then designed for multi-agent systems with connected graphs. The results are verified by both theoretical demonstration and simulation example. [ABSTRACT FROM AUTHOR]

This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

Constant demand for sustainable mechanisms to perform heavy and risky tasks has driven more robotics innovations to arise. Modular reconfigurable robotics system is one of these promising technologies that are continuously explored. Homogeneous types, to be specific, can accomplish similar missions at the same time as individual and heavier missions as an integrated system. This paper presents an analysis of the carrying capacity of a six-wheeled modular multi-agent system using the symbiotic model. The objective is to determine the resulting symbiotic relationship of a given configuration and module state combinations. The results show that the dominant relationship among the trials for linear traversal mission is commensalism. That means, the system neither benefits nor gets harmed from the symbiosis formed. This is true both in simulated and actual test environments although the percentage difference is about 12%. MATLAB Simulink was used for simulation while Maqueen robot in a 3D-printed chassis was used for actual testing. With this study, future configurations for several other missions such as object tracking and ramp climbing can be assessed using the same approach so that possible fault occurrence during operations can be prevented since the developed analysis method is performed prior to the deployment of the system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel decentralized approach for multi-stage job-shop scheduling. The method divides the larger job-shop scheduling problem into several smaller problems, which various agents solve. The collaboration among these agents ensures the exploration of globally superior solutions while allowing enhanced local exploration. Based on an extensive investigation, the current work shows that the proposed approach outperforms the centralized approach, especially for problems with increasing problem size, in a faster manner. Since the proposed approach is based on decentralized information processing, it is easily adaptable to next-generation cyber-physical manufacturing systems. [ABSTRACT FROM AUTHOR]

In today's world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium's behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs. [ABSTRACT FROM AUTHOR]

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include "integrated energy system", "multi-energy system", or "smart energy system". These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process. [ABSTRACT FROM AUTHOR]

• This paper studies the leader-following flocking problem of the unmanned aerial vehicle (UAV). • It employs hybrid event-triggered control and pinning mechanism with delayed control inputs. • Criteria on flocking stability and Zeno behaviour prevention are derived. • It combines both displacement-based control and gyroscopic forces. • The tolerance of network instability in terms of continuous part is increased. This paper studies the leader-following flocking control problem of unmanned aerial vehicle (UAV) systems by integrating a hybrid impulsive framework, where the full exchange of information only occurs at each impulsive time instant. It is also shown that the overall stability can be achieved via impulses while the continuous dynamics remains destabilizing. Meanwhile, the even-triggered and pinning mechanisms are considered to further reduce control resource usage and transmission redundancy. Moreover, topology switching and strong nonlinearity are taken into account for more practical application. Based on transmission topology structure, impulsive control theory, and Lyapunov based event-triggered strategy, some improved theorems are derived to guarantee convergence of the corresponding error dynamics without exhibiting Zeno behaviour. Compared with most existing results on continuous or impulsive systems, the obtained stability criteria are more general as they are applicable to systems involving both delayed coupling feedback and delayed impulses. In addition, a novel approach using braking and gyroscopic forces is utilized for collision avoidance purposes. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Effective exploration is the key to achieving high returns for reinforcement learning. Agents must explore jointly in multi-agent systems to find the optimal joint policy. Due to the exploration problem and the shared reward, the policy-based multi-agent reinforcement learning algorithms face policy overfitting, which may lead to the joint policy falling into a local optimum. This paper introduces a novel general framework called Learning Joint-Action Intrinsic Reward (LJIR) for improving multi-agent reinforcement learners' joint exploration ability and performance. LJIR observes agents' state and joint actions to learn to construct an intrinsic reward online that can guide effective joint exploration. With the novel combination of Transformer and random network distillation, LJIR selects the novel states to give more intrinsic rewards, which help agents find the best joint actions. LJIR can dynamically adjust the weight of exploration and exploitation during training and keep the policy invariance finally. To ensure LJIR seamlessly adopts existing MARL algorithms, we also provide a flexible combination method for intrinsic and external rewards. Empirical results on the SMAC benchmark show that the proposed method achieves state-of-the-art performance in challenging tasks. [ABSTRACT FROM AUTHOR]

Air-to-air combat system is a complex multi-agent system (MAS) wherein a large number of unmanned combat aerial vehicles learn to combat with their opponents in a highly dynamic and uncertain environment. Because of the local observability of each individual, it is difficult for classical multi-agent learning methods to get effective cooperative strategies. Recently, a communication mechanism has been proposed to solve the local observability issue of MAS. However, existing methods with predefined rules easily cause an exponential increase in state–action pairs, leading to high communication costs. Taking this cue, this paper designs a graph neural network based on a two-stage graph-attention mechanism to capture the key interaction relationships and communication connections between agents in complex air-to-air combat scenarios. Based on an essential backbone multi-agent reinforcement learning method, known as Multi-Agent Proximal Policy Optimization, the proposed method with a hard- and soft-attention scheme can realize the dynamic adjustment of the communication relationship and ad hoc network of multiple agents, by cutting off the unrelated interaction connections while building the correlation importance between pair agents, concurrently. Last but not least, the experimental study in the simulation environment has validated the significance of our proposed method in solving the large-scale air-to-air combat problems. [ABSTRACT FROM AUTHOR]

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus problem of the multi-agent system by means of the sliding mode control (SMC) approach. For each agent, the component-based weighted try-once-discard protocol is used for scheduling the transmission of error signals via communication network to the controller. Only one node with the greatest difference at each moment can send its information through the network to the controller. Then, taking the protocol's impact into account, a token-dependent sliding mode controller is designed. Both the asymptotic stability of the closed-loop error system and the reachability of the specified sliding surface are achieved. In addition, by utilising the optimised solving algorithm, the controller gains are attained. Eventually, a simulation example proves the effectiveness of the suggested SMC approach. [ABSTRACT FROM AUTHOR]

A multi-agent system is a system of multiple interacting entities, known as intelligent agents, who possibly have different information and/or diverging interests. The agents could be robots, humans, or human teams. Opinion dynamics is a process of individual opinions in which a group of interacting agents continuously fuse their opinions on the same issue based on established rules to reach a consensus at the final stage. Historically, the idea of reaching consensus through repeated averaging was introduced by DeGroot for a structured time-invariant and synchronous environment. Since then, consensus, which is the most ubiquitous phenomenon of multi-agent systems, has become popular in various scientific fields such as biology, physics, control engineering, and social science. To some extent, a Krause mean process is a general model of opinion sharing dynamics in which the opinions are represented by vectors. In this paper, we represent opinion sharing dynamics by means of Krause mean processes generated by diagonally primitive cubic doubly stochastic matrices, and then we establish a consensus in the multi-agent system. [ABSTRACT FROM AUTHOR]

In this paper, the problem of circle formation of multi-agent system in a plane is investigated. Inspired by the idea of decouple design, we decompose the overall control objective into two sub-goals, the first one is circle forming that agents need to move to a circle with a desired radius and centred on a predefined target, and the second one is space adjustment that each agent form and maintain a desired relative position with its neighbours. Since the completion of the second objective requires information from neighbouring agents, a distributed event-triggered controller is proposed to save communication resources and reduce the number of controller updates, which consists of two parts corresponding to two sub-goals. Furthermore, it is proved that there exists no Zeno behaviour under the proposed control laws. Finally, a numerical simulation verifies the effectiveness of the distributed event-triggered circle formation controller proposed in this paper. [ABSTRACT FROM AUTHOR]

Abnormal combustion is an important factor in the development process of hydrogen engine and it mainly includes pre-ignition, backfire and knocking, among which pre-ignition has the most serious impact on hydrogen engine. In this paper, it is divided into four types: normal combustion, slight pre-ignition, moderate pre-ignition and severe pre-ignition according to different crankshaft rotation angles. In order to identify different combustion types, this paper proposes a fault diagnosis model based on the fusion of SOM neural network and Multi-Agent System (SOM-MAS). Firstly, different combustion types are identified by SOM. Secondly, the abnormal combustion is tracked and located mainly through the Multi-Agent System, and the location of the abnormality is identified. Finally, based on 44 sets of pressure data samples collected from the in-cylinder combustion of a hydrogen engine on the experimental bench, different combustion types were diagnosed and identified, and the location of abnormal combustion faults was tracked, which verifies the effectiveness of the proposed method shows that the method has certain feasibility and superiority for the diagnosis of hydrogen engine pre-ignition. • Research carried out by the 492Q hydrogen engine, close to actual conditions. • The dataset is original and contains data for normal and abnormal combustion. • SOM-MAS can be applied in abnormal combustion of hydrogen fuel engines. • Less relevant dataset, we will publish the dataset for future research. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-step broadcast control (MBC) scheme to deploy a group of autonomous mobile agents for accomplishing coverage tasks in a bounded region. Traditional broadcast control (BC) schemes use a one-to-all communication framework to transmit a uniform signal to all agents, making it cost-effective compared with any all-to-all communication-based scheme for a multi-agent system. However, as BC schemes are based on a single-step view of the environment for decision-making, the environment's varying distribution density is not known immediately to the agents, resulting in suboptimal performance. To overcome this drawback, this paper proposes an MBC scheme, where agents use a predictive multi-step view and are able to detect the varying densities in the environment ahead of time. The local controller output is estimated using a weighted averaging technique which assigns a higher weight to immediate steps; this feature compensates for any decrease in prediction accuracy as the number of steps increases. We demonstrate the effectiveness of the proposed MBC scheme using a coverage task over a region with uneven population density. Compared to existing BC schemes, the proposed MBC scheme shows superior convergence characteristics in task accomplishment and deployment efficiency. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of a new multi-agent system via multi-task, multi-control mechanism and multi-consensus strategy. Firstly, two multi-task algorithms are proposed, which can reasonably select some agent nodes with better performance to form the multi-agent system according to different tasks. Secondly, a multi-control protocol designed in this paper integrates continuous control mechanism, impulsive control mechanism and hybrid control mechanism, which has strong applicability and efficiency. Thirdly, a multi-consensus strategy is designed, and it can be converted into "average consensus strategy", "leader-following consensus strategy" and "hybrid consensus strategy" through different parameter settings, which greatly broadens the application field of the multi-agent system. We construct a dynamic model for the proposed multi-agent system, analyze it by using matrix measure and Lyapunov stability theory, and obtain the theorems that enable it to achieve multiple consensus. Finally, the efficiency and practicability of our theories are verified through several simulation experiments. We further carry out some comparative analysis with some relevant papers, and obtain the superiority of our theories in multiple indicators. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the fault-tolerant consensus problem for leader-following heterogeneous multi-agent systems with actuator faults. The model of multi-agent systems is reconstructed and the lumped faults, including external disturbances and actuator faults, are constructed. A new adaptive super-twisting sliding mode observer (ASTSMO) is constructed to estimate the undetectable lumped faults. The time-varying gain is introduced to solve the initial error problem and peak value problem. Then, based on the estimated results, an integral terminal sliding mode controller (ITSMC) is designed by integrating the fixed-time stability theory, which can effectively eliminate the nonlinear term and lumped faults. Finally, simulations are provided to illustrate the effectiveness and benefits of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

The optimal dispatch of reactive power is a fundamental task in the operational planning of electrical power systems. This task aims to minimize active power losses and improve voltage levels within the electrical power system. This paper presents the application of the particle swarm optimization methodology to achieve optimal reactive power dispatch. The methodology's performance is demonstrated by its high processing speed and the results obtained through a comprehensive global search for reactive power dispatch. Additionally, experimental results confirm the algorithm's effectiveness in optimizing the objective function across different case studies, highlighting its ability to achieve optimal reactive power dispatch. This study represents a significant advancement in the field of power system optimization and provides a useful tool for managing and controlling these systems. [ABSTRACT FROM AUTHOR]

In a socialized multi-agent system, the preferences of individuals will be inevitably influenced by others. This paper introduces an extended empathy structure to characterize the coupling process of preferences under specific relations and make it cover scenarios including human society, human–machine system, and even abiotic engineering applications. In this model, empathy is abstracted as a stochastic experience process in the form of Markov chain, and the coupled empathy utility is defined as the expectation of obtaining preferences under the corresponding probability distribution. The self-other separation is the core concept with which our structure can exhibit social attributes, including attraction of implicit states, inhibition of excessive empathy, attention of empathetic targets, and anisotropy of the utility distribution. Compared with the previous empirical models, our model has a better performance on the data set and can provide a new perspective for designing and analyzing the cognitive layer of the human–machine network, as well as the information fusion and semi-supervised clustering methods in engineering. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential consensus problem for a class of nonlinear leader-following multi-agent systems using impulsive control, where impulses are generated by the event-triggered mechanism and are subjected to actuation delays. It is proved that Zeno behavior can be avoided, and by employing the linear matrix inequality technique, some sufficient conditions for realizing exponential consensus of the considered system are derived. Actuation delay is an important factor affecting the consensus of the system, and our results show that increasing the actuation delay can enlarge the lower bound of the triggering interval, while it harms the consensus. To demonstrate the validity of the obtained results, a numerical example is provided. [ABSTRACT FROM AUTHOR]

The design of agents interacting with human beings is becoming a crucial problem in many real-life applications. Different methods have been proposed in the research areas of human–computer interaction (HCI) and multi-agent systems (MAS) to model teams of participants (agents and humans). It is then necessary to build models analyzing their decisions when interacting, while taking into account the specificities of these interactions. This paper, therefore, aimed to propose an explicit model of such interactions based on game theory, taking into account, not only environmental characteristics (e.g., criticality), but also human characteristics (e.g., workload and experience level) for the intervention (or not) of agents, to help the latter. Game theory is a well-known approach to studying such social interactions between different participants. Existing works on the construction of game matrices required different ad hoc descriptors, depending on the application studied. Moreover, they generally focused on the interactions between agents, without considering human beings in the analysis. We show that these descriptors can be classified into two categories, related to their effect on the interactions. The set of descriptors to use is thus based on an explicit combination of all interactions between agents and humans (a weighted sum of 2-player matrices). We propose a general model for the construction of game matrices based on any number of participants and descriptors. It is then possible to determine using Nash equilibria whether agents decide (or not) to intervene during the tasks concerned. The model is also evaluated through the determination of the gains obtained by the different participants. Finally, we illustrate and validate the proposed model using a typical scenario (involving two agents and two humans), while describing the corresponding equilibria. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) resources' installation has numerous advantages in distribution feeders nonetheless they may threaten the selectivity and reliability of protective installations. All proposed schemes including employing distance or directional overcurrent relays, mathematics methods, online overcurrent relay settings change, fault current limiters, PV disconnection after fault, computing optimal installation location of PVs, and the others are too costly or complex or inefficient in PV-penetrated feeders. This paper proposes a new central processor unit to create a smart overcurrent relay and prohibit protection blinding, nuisance tripping, unwanted tripping, and synchronous tripping of overcurrent relay in a PV-penetrated distribution feeder. Firstly, the processor consists of four layers of the multi-agent system, which receives information on the relays and sensed current via the grid. Secondly, each agent is responsible to prevent a separate maloperation event and scrutinizing the input information. Finally, it directs the manipulated commend to the respective breaker if relay has acted incorrectly. Overcurrent relays become smart and miscoordination and maloperation problems are corrected completely by implementing the proposed processor. The proposed scheme is fast, offline, inexpensive, independent of the fault and PV installation location, type and impedance of the fault, and PV capacity. The results indicate the proposed plan has acted successfully with 100% efficiency in all scenarios. [ABSTRACT FROM AUTHOR]

In modern smart manufacturing, robots are often connected via a network, and their task can change according to a predetermined program. Iterative learning control (ILC) is widely used for robots executing high-precision operations. Under network conditions, the efficiency of ILC algorithms may decrease if the program is restructured. In particular, the learning error may temporarily increase to an unacceptable value when changing the reference trajectory. This paper considers a networked system with the following features: the reference trajectory and parameters change between passes according to a known program, agents are subjected to random disturbances, and measurements are carried out with noise. In addition, the network topology changes due to the disconnection of some agents from the network and the connection of new agents to the network according to a given program. A distributed ILC design method is proposed based on vector Lyapunov functions for repetitive processes in combination with Kalman filtering. This method ensures the convergence of the learning error and reduces its increase caused by changes in the reference trajectory and network topology. The effectiveness of the proposed method is confirmed by an example. [ABSTRACT FROM AUTHOR]

In this paper, a distributed adaptive finite-time consensus (FTC) control protocol for a high-order multi-agent system (MAS) with intermittent communications under switching topologies is proposed. Meanwhile, considering the problem of heterogeneous unknown nonlinearities and other uncertain disturbances, the adaptive neural network and the sliding mode control method are used to compensate the nonlinearity of each agent separately. The agents are homogeneous, so the system has symmetry. The switching topologies considered in this paper are asymmetric. Compared with consensus protocol for asymptotic convergence, simulation results show that the proposed method can effectively solve the presence of the nonlinear and accelerate the convergence speed of the system so that an FTC can be reached. [ABSTRACT FROM AUTHOR]

In this paper, a multi agent recommender system is designed and developed for user interests extraction The system consists of eight agents such as age, identity, personality, social, financial, location, and needs. The agents works with each others in a collaborative way to make recommendation to the users according to their interest. The relation between the agents and the users are controlled by a well developed protocol and pre-defined senses. The information between the users and the agents are collected in information center agent (ICA). The data collected in ICA can be used to rearrange the videos in way such that it is more relative to the user depending on his interest. This interest can be extracted from the information that the user initially provides to the system which can be then analyzed from the multi agent system to decide whether the user is interested in a video or not. This is done by creating video -important term matrix, user important term matrix and agent -feature matrix. Then, theses matrices are used by the multi-agent system to get video-Agent effective matrix for the users which leads to most ordered videos in order to be presented to the user. The proposed model was verified by intensive simulations using eight agents using JADE platform. The results show that the accuracy of the system for 50 videos that were well arranged for 40 users is 87%. [ABSTRACT FROM AUTHOR]

Summary: Distributed optimization algorithms have various advantages and wide applications, while the distributed nature also makes them flimsy in the face of external attacks. In this paper two Byzantine attack models are designed, in which the edge attack model is seldom considered before. A resilient distributed consensus algorithm based on the trusted nodes and edges is proposed to defend against the two attack models. The agreement and optimality of the proposed consensus algorithm are guaranteed with theoretical analysis. The simulation results and comparison experiments verify the proposed algorithm to be more effective than the compared one. In the perspective of attackers, which attack model is more recommended is also analysed based on the experiments. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more "cohesive", thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase. [ABSTRACT FROM AUTHOR]

In recent years, the development of smart cities has accelerated. There are several issues to handle in smart cities, one of the most important of which is efficient resource allocation. For the modeling of smart cities, multi-agent systems (MASs) can be used. In this paper, an efficient approach is proposed for resource allocation in smart cities based on the multi-agent credit assignment problem (MCA) and bankruptcy game. To this end, the resource allocation problem is mapped to MCA and the bankruptcy game. To solve this problem, first, a task start threshold (TST) constraint is introduced. The MCA turns into a bankruptcy problem upon introducing such a constraint. Therefore, based on the concept of bankruptcy, three methods of TS-Only, TS + MAS, and TS + ExAg are presented to solve the MCA. In addition, this work introduces a multi-score problem (MSP) in which a different reward is offered for solving each part of the problem, and we used it in our experiments to examine the proposed methods. The proposed approach is evaluated based on the learning rate, confidence, expertness, efficiency, certainty, and correctness parameters. The results reveal the better performance of the proposed approach compared to the existing methods in five parameters. [ABSTRACT FROM AUTHOR]

• Agents communicate indirectly and asynchronously via a shared cloud repository. • Both globally and locally Lipschitz nonlinearity in system dynamics are discussed. • Adaptive control scheme is considered for multi-agent systems under directed graph. • Practical tracking consensus is achieved for multi-agent systems with disturbances. • Zeno-freeness is guaranteed for the proposed cloud-based self-triggered scheme. This paper addresses the practical tracking consensus problem for first-orderheterogeneous nonlinear multi-agent systems in presence of external disturbances under directed graphs. Agents with limited communication capability interact with each other through a cloud repository, where the information exchange is asynchronous and indirect. The system model is more general than the ones in existing results, moreover, locally Lipschitz nonlinearities and adaptive control schemes are taken into account. It is proven that under the designed self-triggered protocol, agents can track a virtual leader with bounded error and Zeno behaviour is excluded for real implementation. Finally, a numerical simulation demonstrates the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Almost all systems are inevitably subject to various uncertainties or disturbances from the external environment in practical applications. Taking these factors into consideration, in this paper a distributed algorithm with state noise and gradient disturbance is proposed for solving distributed optimization problem with closed convex set constraint based on multi-agent system under weight-balanced graph. Moreover, based on the gradient tracking and projection methods, the proposed distributed algorithm with gradient tracking can improve the convergence rate by introducing a projection error term and an auxiliary parameter. In contrast to some existing constrained distributed gradient algorithms, the proposed one can make the convergence faster and enhance the performance of convergence. The proposed algorithm is illustrated with two simulation examples to show its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal consensus problem for heterogeneous discrete-time(DT) linear multi-agent systems. The optimal consensus problem is formulated as finding a global Nash equilibrium solution subjected to the defined local performance index. A reinforcement learning(RL) value iteration(VI) algorithm is introduced to obtain the optimal policies in the sense of Nash equilibrium. To ensure the effectiveness of the VI algorithm, the admissibility of the iterative control policies for multi-agent systems is considered. With theoretical analysis, a new termination criterion is established to guarantee the admissibility of the iterative control policies. Furthermore, an online learning framework is designed with an actor-critic neural network(NN) to implement the VI algorithm. Finally, two simulation examples are presented respectively for leader–follower and leaderless multi-agent systems to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In modern society, multi-agent consensus is applied in many applications such as distributed machine learning, wireless sensor networks and so on. However, some agents might behave abnormally subject to external attack or internal faults, and thus fault-tolerant consensus problem is studied recently, among which Q-consensus is one of the state-of-the-art and effective methods to identify all the faulty agents and achieve consensus for normal agents in general networks. To fight against Q-consensus algorithm, this paper proposes a novel strategy, called split attack, which is simple but capable of breaking consensus convergence. By aggregating all the states of neighboring nodes with an extra perturbation, the normal nodes are split into sub-groups and converge to two separate values, so that consensus is broken. Two scenarios, including the introduction of additional faulty nodes and compromise of the original nodes, are considered. More specifically, in the former case, two additional faulty nodes are adopted, each of which is responsible to mislead parts of the normal nodes. While in the latter one, two original normal nodes are compromised to mislead the whole system. Moreover, the compromised nodes selection is fundamentally a classification problem, and thus optimized through CNN. Finally, the numerical simulations are provided to verify the proposed schemes and indicate that the proposed method outperforms other attack methods. [ABSTRACT FROM AUTHOR]

Leader–follower consensus control is a kind of consensus control, where some special agents, called the leaders, have relatively high flexibility in changing their states, and the other agents, called the followers, follow the leaders. One of the important issues for leader–follower consensus control is a leader selection problem, i.e. determining leaders to achieve a desired performance. On the other hand, the notion of a feedback vertex set is well-known in graph theory. Feedback vertex sets are known to play an important role in various problems, e.g. deadlock avoidance and sensing node selection of biological networks. Thus, they are expected to be closely related to leader–follower consensus control. However, their relationship has never been studied so far. This paper discloses the relationship between leader selection and feedback vertex sets. We deal with a problem of finding a minimum leader set to achieve the fastest convergence to consensus and show that a solution to the leader selection problem is given by a feedback vertex set. [ABSTRACT FROM AUTHOR]

This paper presents a distributed input rectification methodology to guarantee the safety of a multi-agent system over its pre-given cooperative control task. We start with defining a multi-agent system as a control target, which consists of a group of agents with general control affine nonlinear dynamics and an interconnection topology between agents. The research objective, safety, is next defined via a set invariance property, where a safe set is formed by control barrier functions. Then, an optimization-based distributed safety control method is presented. In the proposed approach, each agent rectifies its control input by solving an individual optimization problem to achieve a pre-given cooperative control strategy as much as possible while satisfying constraints for the safety derived by investigating the control barrier functions. Here, the optimization problem is formulated in a distributed manner by dividing the safety constraint between agents. To show the utility of the present approach, collision avoidance problems of a mobile robotic network are investigated as case studies. [ABSTRACT FROM AUTHOR]

The on-demand transport (ODT) systems have developed worldwide as they have significant social, environmental, and economic benefits. Even with those benefits, it's still important to gain popular acceptance. The acceptance key is the reactivity of the system in providing fast and reliable solutions whilst respecting vehicles' and clients' constraints. This paper presents a decentralized multi-agent approach to model and solve the ODT problem in a static road network. The agents interact with each other using the A* algorithm to find an optimal solution for each transport demand. The optimal solution is expressed by the fastest trajectory taken by the cheapest vehicles. We utilize factual data from a Lebanese city to do experiments evaluating the proposed approach. [ABSTRACT FROM AUTHOR]

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action. [ABSTRACT FROM AUTHOR]

Generation expansion planning (GEP) is a widely studied problem in the literature. However, with increasing participation of renewable energy sources (RES), the problem has to be looked upon considering intermittency associated with these sources. Integration of storage devices is seen as a viable option for counteracting the intermittency, particularly in isolated micro-grid (IMG). This paper puts forth a generalized mathematical structure for GEP centred on techno-economic criteria which duly addresses uncertainties associated with RES and correspondingly proposes a plan for addition of RES and storage units. As GEP problem is a highly constrained, large-scale problem involving assessment of enormous combinations, a decentralized multi-agent system (MAS) is used along with butterfly particle swarm optimization (BFPSO) technique for parallel processing. Hybridization of features of MAS with BFPSO called as multi-agent-based butterfly PSO algorithm (MABFPSO) presents a time effective and more accurate alternative to existing optimization techniques for addressing GEP problem. The formulation has been applied on IMG comprising of RES (solar and wind) and battery storage system. Results obtained from MABFPSO are compared with PSO with the purpose of establishing superiority of MABFPSO. It has been found that MABFPSO provides a cost benefit of 3.5% in comparison with classic PSO. For the same reliability standards, the capacity addition required for solar and wind generators is similar for both the techniques. However, the storage capacity requirement with PSO is 100% higher in comparison with that of MABFPSO. The comparison of convergence characteristics of two techniques clearly demonstrates that on the iteration scale, MABFPSO presents 29.6% faster convergence in comparison with that of PSO. [ABSTRACT FROM AUTHOR]

In this paper, a novel energy management method that is based on a Multi-Agent System (MAS) is presented for hybrid Distributed Energy Sources (DES) in a microgrid. These DESs include Photovoltaic (PV), wind energy systems, and Fuel Cell (FC) in the Microgrid (MG). The MG is responsible for supplying both active and reactive powers, allowing it to serve variable linear and non-linear loads. The MAS that has been proposed and is based on a decentralized control structure offers control not only for the energy management of the Distributed Generation (DG) but also for the management of power flow between the MG and the power grid that is connected to the MG. This control is offered by the MAS. The main objective of the control strategy is to manage the amount of energy that is transferred between the power grid and the MG concerning the supply conditions of the required internal energy via DES, which will ultimately result in a reduction in the dependence on the MG on the grid. For current harmonic compensation, a Static Compensator (STATCOM) with a Fuzzy Logic (FL) based Instantaneous Reactive Power control scheme is used. On the other hand, a discrete controller is utilized to manage the energy of the MG. The findings of the simulation and the experiments demonstrated that the implementation of the suggested Energy Management System (EMS) has good performance as a novel energy management solution for a hybrid distributed power generating system and harmonic compensation. [ABSTRACT FROM AUTHOR]

Traffic guidance and traffic control are effective means to alleviate traffic problems. Formulating effective traffic guidance measures can improve the utilization rate of road resources and optimize the performance of the entire traffic network. Assuming that the traffic coordinator can capture traffic flow information in real-time utilizing sensors installed on each road, we consider the strong resilience constraints to construct an optimal selection problem of balanced flow in the traffic network. Based on multi-agent modeling, each agent has access to the corresponding traffic information of each link. We design a distributed optimization algorithm to tackle this optimization problem. In addition to the inherent advantages of distributed multi-agent algorithms, the communication topology among the sensors is allowed to be time-varying, which is more consistent with reality. To prove the effectiveness of the proposed algorithm, we also give a numerical simulation in the multi-agent environment. It should be reiterated that the optimization problem studied in this paper mainly focuses on traffic managers' perspectives. The goal of the studied optimization problem is to minimize the overall cost of the traffic network by adjusting the optimal equilibrium traffic flow. This study provides a reference for solving congestion optimization in today's cities. • Formulate an equilibrium flow selection problem with resilience constraints. • The distributed algorithm allows the communication among agents to be time-varying. • Communication graph among agents allows for being B-connected. [ABSTRACT FROM AUTHOR]

Consensus problem with faster convergence rate of consensus problem has been considered in this paper. Adding more edges such as that connecting each agent and its second-nearest neighbor or changing the consensus protocol such as mixing asymptotic terms and terms of finite-time has been proved to be possible ways in increasing the convergence rate of multi-agent system in this paper. Based on analysis of Laplacian matrix, increasing of the convergence rate has been proved using the second-smallest eigenvalue for the first method. Concerning the second method, advantages of asymptotic consensus protocol and finite-time consensus protocol have been mixed together with the help of homogeneity function and theory of Lyapunov. Simulation results using matlab are also presented to illustrate the newly designed consensus protocols in increasing the convergence rate. [ABSTRACT FROM AUTHOR]

Distribution system service restoration is a process of restoring power outages through changing the on/off status of sectionalizing and tie switches within the whole power outage in a distribution system altered accordingly with its topological structure. Multi-agent Systems (MASs) can be applied for distribution system service restoration, an engineering optimization problem that can be addressed through metaheuristics, of a distribution system because service restoration planning can be made in parallel among intelligent software agents embedded inside sectionalizing and tie switches and feeders to form a distributed multi-agent environment such that the time of power restoration can be reduced. Service restoration planning can be built upon an MAS. This paper presents a three-tiered MAS-based Multi-Population Parallel Genetic Algorithm (MPPGA) and demonstrates its preliminary implementation to achieve service restoration planning for smart distribution system service restoration, which serves as a meta service restoration planner to perform efficient and fast switching operation for smart distribution system service restoration. The presented three-tiered MAS-based MPPGA is implemented, in Java programming language, on a Java Agent DEvelopment (JADE for short) platform, where in evolutionary computation (1) multiple populations are coevolved and (2) selection, crossover, and mutation operations for genetic search are parallelized. The effectiveness/feasibility of the presented three-tiered MAS-based MPPGA is demonstrated by a simulated distribution system and reported with simulation results. [ABSTRACT FROM AUTHOR]

This paper considers the event-triggered predictive control(ETPC) problem for linear discrete-time multi-agent systems. To achieve accurate state estimation under event-triggered communication between agents, an event-triggered predictive control scheme as well as a novel class of triggering condition are proposed for synchronization of multi-agent systems with single-integrator dynamics, where the control input of each agent no longer remains constant but updates periodically within the time interval between two consecutive triggering instants. Then, the proposed scheme is extended to the general linear multi-agent systems. By comparing with the time-triggered control(TTC) method and the existing event-triggered control(ETC) methods, the control performance of the proposed ETPC scheme is almost the same as that of the TTC approach, and the communication payload between agents is reduced with much less triggering times than existing ETC methods. Finally, two numerical examples are given to verify the theoretical analysis and demonstrate the superiority of the proposed method comparing with some existing methods. [ABSTRACT FROM AUTHOR]