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In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios. [ABSTRACT FROM AUTHOR]

The production control for the mass individualisation paradigm of R&D-stage products is challenging due to the mix-flow and frequently-disturbed environment. With the convergence of the sustainable development goals and the increasing individualised demands in products, resilient manufacturing is envisioned in Industry 5.0 proposition. Concerning that conventional centralised production control methods suffer from low stability and inefficiency of decisions under frequent disruptions, this paper establishes a blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach for improving the timeliness and adaptability of control towards resilient individualised manufacturing. Firstly, a blockchain-based multi-agent system architecture is designed based on agent encapsulation of manufacturing units. Blockchained smart contracts are used as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks. Secondly, a quad-play blockchained smart contract pyramid together with a series of decentralised control patterns are designed to enable the initial task dispatching of various individualised demands, as well as rapid dynamic adjustment of schedule in response to internal random disruptions. Finally, a blockchained smart contract pyramid-driven multi-agent autonomous process control system prototype is built in the ManuChain system, and experiments are conducted to analyze the proposed BSCP-MAAPC approach in different environments. HIGHLIGHTS A blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach. A quad-play blockchained smart contract pyramid together with a series of decentralized control patterns. Blockchained smart contracts as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks. [ABSTRACT FROM AUTHOR]

The distributed estimation technology is prevalently utilized to solve the leader‐following multi‐agent tracking problem. This technology poses a challenge in practice, since it generally relies on the available absolute state measurements. For this reason, a novel distributed estimation approach based on relative state measurements is developed in this article. The proposed method directly estimates the tracking error between the leader and each follower, rather than using an existing indirect way of estimating and making subtraction under absolute state measurements. Specifically, a distributed directed estimation is first studied to complete estimation tasks within prescribed time under the known directed networks. Then, a fully distributed directed estimation problem is considered under the unknown directed networks. Both distributed and fully distributed results are extended to the robustness cases to resist external disturbances. Simulation examples, including numerical examples and a multiship coordination example, are provided to demonstrate the effectiveness and advantages of the proposed distributed estimation method. [ABSTRACT FROM AUTHOR]

Plug & Produce aims to revolutionize manufacturing by enabling seamless machine integration into production processes without extensive programming. This concept, leveraging multi-agent systems (MAS), offers increased flexibility and faster production ramp-up times after reconfiguration. As automated manufacturing moves towards greater human integration, this paper addresses safe operation within the Plug & Produce concept. The main safety challenge arises from autonomous decision-making, as agents in the MAS lack awareness of the risk consequences of their behavior. Additionally, the difficulty of perceiving the system's exact behavior leads to the implementation of overly restrictive safety measures. This limits the system's flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control to conduct automatic safety analysis and reason task allocations to avoid risks. The method's benefits are the generation of control actions that comply with safety requirements during operation, eliminating the need for overly restrictive safety measures and allowing more effective equipment utilization. The method's benefit is illustrated through a manufacturing scenario with two different configurations: one using a hazardous machine and the other using a less hazardous one. Formal verification using the model checker NuSMV demonstrated that safety requirements were satisfied in both configurations, without the need for manual modifications of the safety control system after reconfiguration. The results for this specific manufacturing scenario showed that there are more reachable states (20 states) in the safer second configuration, compared to the first configuration (16 states). This means that the presented control strategy dynamically adjusts the system's behavior to confirm safety. Hence, this method maintains safety without fixed safety rules that limit the operations. [ABSTRACT FROM AUTHOR]

Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least 15 k m from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands. [ABSTRACT FROM AUTHOR]

Time-delay is an unavoidable factor in analyzing and designing any networked system since in most scenarios, it decreases the performance or even destabilizes the system. In this paper, we study the newly proposed matrix-scaled consensus (MSC) algorithms under the presence of communication time delays. Both networks of single- and double-integrator agents are considered, and for each scenario, a corresponding delay margin will be given. The analysis hinges on the Nyquist stability criteria and the algebraic solution of quasi-polynomials associated with the MSC models. Numerical examples are provided to demonstrate the correctness of theoretical results. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm, and the introduction of a double experience pool to improve the data utilization rate; at the same time, in order to accelerate the training speed and efficiency of the agents, and to solve the cold-start problem of the training, the a priori knowledge technology is applied to the training of the algorithm. Finally, the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation. The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards, can utilize the resources more reasonably, and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space. The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA. [ABSTRACT FROM AUTHOR]

The operation of multi‐agent systems (MAS) in space necessitates considerations for obstacle avoidance, collision prevention, and connectivity among agents. This coupling conflict, stemming from spatial resource utilization, poses a significant and non‐negligible threat to the safe operation of MAS. This article proposes a consensus control scheme for the time‐varying formation of MAS, incorporating functions for maintaining connectivity, collision avoidance, and obstacle dodging. This scheme effectively constrains the time‐varying formation tracking error within an arbitrarily small range. The considered system model takes a high‐order nonlinear form, with uncertainties and disturbances present in each order. This grants the control scheme with high generality and practicability. By employing barrier Lyapunov function to delineate the safe operational space of MAS, conflicts in spatial resource utilization are avoided. This approach simultaneously fulfills the requirements for connectivity maintenance, collision avoidance, and obstacle dodging in the safe operation of MAS. An additional rotation operator is integrated into the controller to smoothly address the “minima” problem, eliminating the need for external intervention. Gaussian radial basis function is used to estimate the nonlinear terms, uncertainties, and unknown perturbation online in the system. The stability of the MAS under the proposed control scheme is analyzed through Lyapunov function. Finally, numerical simulation results are demonstrated to explain the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

This paper explores the group consensus problem of second-order multi-agent systems (MAS) under asynchronous denial-of-service (DoS) attacks. Asynchronous DoS attacks involve the interruption of certain communication links, allowing the MAS to be reimagined as a switching system with a persistent dwell time (PDT). The agents in each group can be divided into three types, which are inter-act agents, intra-act agents with zero in-degree, and other agents. Then, according to the properties of the different agents, suitable agents are pinned. By leveraging the concepts of switching topology and the PDT, a suitable event-triggered control protocol is designed, along with the establishment of conditions to ensure the group consensus of the MAS. Moreover, through the construction of topology-dependent Lyapunov functions, the achievement of group consensus within the MAS under asynchronous DoS attacks is demonstrated. Subsequently, a numerical example is presented to validate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, achieving this initiative requires careful consideration, especially regarding the complex interactions among multiple stakeholders in the Indonesian electricity market. The electricity market in Indonesia is characterized by its non-competitive and heavily regulated structure. This market condition often requires the PLN, as the system operator, to address multi-objective and multi-constraint problems, necessitating optimization in the generation dispatch scheduling scheme to ensure a secure, economical, and low-carbon power system operation. This research introduces a multiparadigm approach for GS optimization in a regulated electricity market to support the transition to clean energy. The multiparadigm integrates multi-agent system and system dynamic paradigms to model, simulate, and quantitatively analyze the complex interactions among multiple stakeholders in the Indonesian regulated electricity market. The research was implemented on the Java–Madura–Bali power system using AnyLogic 8 University Researcher Software. The simulation results demonstrate that the carbon policy scheme reduces the system's carbon emissions while increasing the system's cost of electricity. A linear regression for sensitivity analysis was conducted to determine the relationship between carbon policies and the system's cost of electricity. This research offers valuable insights for policymakers to develop an optimal, acceptable, and reasonable power system operation scheme for all stakeholders in the Indonesian electricity market. [ABSTRACT FROM AUTHOR]

A multi-area power system requires coordination to enhance reliability and reduce operating costs. Economic dispatch in such systems is crucial because of the uncertainties associated with variable loads and the increasing penetration of renewable energy sources. This paper presents a hierarchical consensus algorithm designed to determine the economic dispatch in a multi-area power system, accounting for the uncertainties in load and renewable generation. The proposed algorithm, which utilizes distributed agents, operates across three levels. Level 1 coordinates all areas, while levels 2 and 3 form a leader–follower consensus algorithm for overall economic dispatch. Breadth-first search is employed to identify the leader agent within each area. To address the uncertainties in loads and renewable generation, Monte Carlo simulations are performed. The efficacy of the proposed method is validated using the IEEE 39-bus and 118-bus systems, as well as a realistic 1968-bus power system in Taiwan. The traditional equal lambda method is employed to verify that the proposed approach is suitable for multi-area power systems using distributed computation. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are emerging as a fundamental brick of engineering Cyber-Physical Systems (CPSs), but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or applications (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and CPSs, and the former could both fill in some gaps in agent-oriented software engineering and benefit from an agent-oriented interpretation—in a cross-fertilisation journey. [ABSTRACT FROM AUTHOR]

A Digital Twin (DT) is a frequently updated virtual representation of a physical or a digital instance that captures its properties of interest. Incorporating both cyber and physical parts to build a digital twin is challenging due to the high complexity of the requirements that should be addressed and satisfied during the design, implementation and operation. In this context, we introduce the MADTwin (Multi-Agent Digital Twin) framework driven by a Multi-agent Systems (MAS) paradigm and supported by flexible architecture and extendible upper ontology for modelling agent-based digital twins. A comprehensive case study of a smart warehouse supported by multi-robots has been presented to show the feasibility and applicability of this framework. The introduced framework powered by intelligent agents integrated with enabler technologies enabled us to cope with parts of the challenges imposed by modelling and integrating Cyber-Physical Systems (CPS) with digital twins for multi-robots of the smart warehouse. In this framework, different components of CPS (robots) are represented as autonomous physical agents with their digital twin agents in the digital twin environment. Agents act autonomously and cooperatively to achieve their local goals and the objectives of the whole system. Eventually, we discuss the framework's strengths and identify areas of improvement and plans for future work. [ABSTRACT FROM AUTHOR]

This paper presents a novel leader-follower consensus control for a particular class of nonlinear multi-agent mechanical systems in the presence of control input constraints and external disturbances which includes robot system dynamics with a wide range of potential applications in industry. In this case, one of the agents is selected as the leader to direct the other agents in such a way that the whole system can reach consensus within certain prescribed performance transient bounds. Due to the presence of disturbances in most practical systems, the effect of limited disturbance in the consensus control method has been investigated, and actuator saturation is included in the design process. A terminal sliding mode control method has been adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in MATLAB environment, in different scenarios with simultaneous consideration of actuator saturation and external disturbance, will show the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the secure output consensus problem for the heterogeneous multi-agent systems under the event-triggered scheme in the presence of the denial-of-service attack. Without detecting the attack, the hold-input controller update strategy is adopted when some transmission data may be lost due to the effect of the attack. Based on the tolerable duration of the attack, a novel edge-based event-triggered scheme is developed. The scheme can avoid continuous communication and exclude Zeno behavior. With the aid of the switched system theory, output consensus is preserved. An example shows the effectiveness. • Secure problem for the heterogeneous multi-agent systems is solved in the presence of the denial-of-service attack. • A novel edge-based event-triggered scheme is developed based on the tolerable duration of the attack. • Without detecting the attack, the hold-input controller update strategy is adopted. • Output consensus is preserved with the aid of the switched system theory. [ABSTRACT FROM AUTHOR]

The objective of the invariant ellipsoid method is to minimize the smallest invariant and attractive set of a linear control system operating under the influence of bounded external disturbances. This study extends the application of this method to address the leader-following consensus problem. Initially, a linear control protocol is designed for the multi-agent system in the absence of disturbances. Subsequently, in the presence of bounded disturbances, by employing a similar linear control protocol, a necessary and sufficient condition is introduced to derive the optimal control parameters for the multi-agent system such that the state of followers converges to and remains in a minimal invariant ellipsoid around the state of the leader. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article investigates the problem of fixed‐time sliding mode consensus tracking for nonlinear second‐order multi‐agent system, in the presence of disturbances and actuator attacks. The interaction topology graph of multi‐agent system is directed and switched, and the switching topologies satisfy a more general persistent dwell‐time switching mechanism. To account for the disturbances, actuator attack, and switching topologies, a nonsingular sliding mode control law with distributed switching scheme is constructed to guarantee the fixed‐time reachability to a sliding surface. By using multiple Lyapunov functions, some new sufficient criteria are derived for fixed‐time consensus tracking of the multi‐agent system with persistent dwell‐time switching topologies. To illustrate the proposed approach, we present a numerical example and a physical example of the single pendulum model. [ABSTRACT FROM AUTHOR]

Rapid growth in a number of developing nations' mobile telecommunications sectors presents network operators with difficulties such as poor service quality and congestion, mostly because these locations lack a dependable and reasonably priced electrical source. In order to provide a sustainable and reasonably priced energy alternative for the developing world, this study provides a detailed examination of the core ideas behind renewable energy technology (RET). A multi-agent-based small-scaled smart base transceiver station (BTS) site reinforcement strategy is presented to manage energy resources by boosting resilience so to supply power to essential loads in peak demand periods by leveraging demand-side management (DSM). Diverse energy sources are combined to create interconnected BTS sites, which enable energy sharing to balance fluctuations by establishing a market that promotes economical energy. A MATLAB simulation model was developed to assess the effectiveness of the proposed system by using real load data and fast electric vehicle charging loads from five different base transceiver stations (BTSs) located throughout Pakistan's southern area. In this proposed study, the base transceiver station (BTS) sites can share their energy through a multi-agent-based system. From the results, it is observed that, after optimization, the base transceiver station (BTS) sites trade their energy with the grid at rate of 0.08 USD/kWh and with other sites at a rate of 0.04 USD/kWh. Therefore, grid dependency is decreased by 44.3% and carbon emissions are reduced by 71.4% after the optimization of the base transceiver station (BTS) sites. [ABSTRACT FROM AUTHOR]

We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency. [ABSTRACT FROM AUTHOR]

This paper investigates the stability and motion of two interactive oscillating agents. Multiple agents can be controlled in a centralized and/or distributed manner to form specific patterns in cooperative tracking, pursuit, and evasion games, as well as environmental exploration. This paper studies the behavior of two oscillating agents due to their interaction. It shows that, through a combination of selecting oscillation centers and interaction gain, a variety of motions, including limit-cycles and stationary behavior, can be realized. [ABSTRACT FROM AUTHOR]

This paper demonstrates several sufficient frameworks for the multi-cluster flocking behavior of the fractional Cucker–Smale (CS) model. For this, we first employ the Caputo fractional derivative instead of the usual derivative to propose the fractional CS model with the memory effect. Then, using mathematical tools based on fractional calculus, we present suitable sufficient conditions in terms of properly separated initial data close to the multi-cluster, and well-prepared system parameters for the multi-cluster flocking of the fractional system to emerge. Finally, we offer several numerical simulations and compare them with the analytical results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We consider the controllability of multi-agent dynamical systems modeled by a particular class of bipartite graphs, called chain graphs. Our main focus is related to chain graphs with exactly one repeated degree. We determine all chain graphs with this structural property and derive some properties of their Laplacian eigenvalues and associated eigenvectors. On the basis of the obtained theoretical results, we compute the minimum number of leading agents that make the system in question controllable and locate the leaders in the corresponding graph. Additionaly, we prove that a chain graph with exactly one repeated degree, that is not a star or a regular complete bipartite graph, has the second smallest Laplacian eigenvalue (also known as the algebraic connectivity) in $ (0.8299, 1) $ and we show that the second smallest eigenvalue increases when the number of vertices increases. This result is of a particular interest in control theory, since families of controllable graphs whose algebraic connectivity is bounded from below model the systems with a small risk of power or communication failures.

This paper presents a novel leader-follower consensus control for a particular class of nonlinear multi-agent mechanical systems in the presence of control input constraints and external disturbances which includes robot system dynamics with a wide range of potential applications in industry. In this case, one of the agents is selected as the leader to direct the other agents in such a way that the whole system can reach consensus within certain prescribed performance transient bounds. Due to the presence of disturbances in most practical systems, the effect of limited disturbance in the consensus control method has been investigated, and actuator saturation is included in the design process. A terminal sliding mode control method has been adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in MATLAB environment, in different scenarios with simultaneous consideration of actuator saturation and external disturbance, will show the efficiency of the proposed control method.

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 matrixs 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.