Search Results (68)

Multi-agent systems (MAS) typically model interaction topologies using directed or undirected graphs when analyzing consensus convergence rates. However, as system complexity increases, purely directed or undirected networks may be insufficient to capture interaction heterogeneity. This paper adopts hybrid networks as interaction topology to investigate strategies for improving consensus convergence rates. We propose the Hermitian Kirchhoff index, a novel metric based on resistance distance, to quantify the consensus convergence rates and establish its theoretical justification. We then examine how adding or removing edges/arcs affects the Hermitian Kirchhoff index, employing first-order eigenvalue perturbation analysis to relate these changes to algebraic connectivity and its associated eigenvectors. Numerical simulations corroborate the theoretical findings and demonstrate the effectiveness of the proposed approach. Full article

While networked multi-agent systems have been widely explored, the challenges introduced by underactuation still impede safety-critical cooperative control of multiple underactuated manipulators. This paper introduces a distributed framework for end-effector formation control and obstacle avoidance in planar n-link manipulators with a passive first joint and active remaining joints—termed PAⁿ⁻¹ manipulators. By exploiting the integrability of each PAⁿ⁻¹ manipulator’s second-order nonholonomic constraint, we reformulate the dynamics into a cascaded structure and derive a reduced-order model driven solely by active joint velocities. Building on this reduced-order model, we design safety-critical distributed formation control laws for the reduced-order dynamics, which serve as the manipulators’ desired active joint velocities. Then, we employ the backstepping method to obtain control inputs for the full-order dynamics. To guarantee safety, we treat backstepping tracking errors as matched disturbances and address them within a robust control barrier function framework. Numerical simulations and comparative studies confirm the effectiveness of the proposed approach. Full article

For multi-agent systems under Denial-of-Service (DoS) attacks, a relative threshold strategy for event triggering and a state-constrained control method with prescribed performance are proposed. Within the framework of combining graph theory with the leader–follower approach, coordinate transformation is utilized to decouple the multi-agent system. Inspired by the three-way handshake technology of TCP communication, a DoS detection system is designed based on event-triggering. This system is used to detect DoS attacks, prevent the impacts brought by DoS attacks, and reduce the update frequency of the controller. Fuzzy logic systems are employed to approximate the unknown nonlinear functions within the system. By using a first-order filter to approximate the derivative of the virtual controller, the computational complexity issue in the backstepping method is addressed. Furthermore, The Barrier Lyapunov Function (BLF) possesses unique mathematical properties. When the system state approaches the pre-set boundary, it can exhibit a special variation trend, thereby imposing a restrictive effect on the system state. The Prescribed Performance Function (PPF), on the other hand, defines the expected performance standards that the system aims to achieve in the tracking task, covering key indicators such as tracking accuracy and response speed. By organically integrating these two functions, the system can continuously monitor and adjust its own state during operation. When there is a tendency for the tracking error to deviate from the specified range, the combined function mechanism will promptly come into play. Through the reasonable adjustment of the system’s control input, it ensures that the tracking error always remains within the pre-specified range. Finally, through Lyapunov analysis, the proposed control protocol ensures that all closed-loop signals remain bounded under attacks, with the outputs of all followers synchronizing with the leader’s output in the communication graph. Full article

The adaptive fuzzy fault-tolerant formation control of nonlinear high-order fully actuated multi-agent systems is studied in this paper, which contains time-varying delays and nonlinear non-affine faults. In contrast to the state-space approach, the proposed control method is based on the fully actuated system approach, which does not require converting a high-order system into a first-order one but directly designs controllers for high-order nonlinear multi-agent systems. The time-varying delays of the systems can be solved using the finite covering lemma and fuzzy logic systems. Compared with the traditional Lyapunov–Krasovskii functional method, the proposed control methodology relaxes the constraint of bounded derivatives for time-varying delays. The problem of algebraic loop in controller design caused by nonlinear non-affine faults is avoided using a Butterworth low-pass filter. Based on the Lyapunov stability theory, the proposed controller methodology is demonstrated to ensure the stability of the closed-loop system, and all followers can keep ideal formation with the leader. Finally, the validity of the theoretical results is demonstrated through three simulation examples, and the design steps of the controller for the simulation examples are reduced by fifty percent compared to the state-space method. Full article

The existing standalone microgrids (MGs) require good energy management systems (EMSs) to respond to energy needs. The EMS presented in this paper is used for an MG based on PV and wind energy sources. The energy storage system is implemented using three packs of batteries. Power smoothing is carried out via the introduction of supercapacitors (SCs) in parallel to the loads and sources. The distribution of energy of the presented MG is focused on the multi-agent system (MAS) using Fuzzy Logic Supervisor control. The MAS is used in order to leverage autonomous and interacting agents to optimize operations and achieve system objectives. To reduce the stress on batteries and avoid damaging all the batteries together by the charge and discharge cycles, one pack of batteries can usually be used. When this pack of batteries is fully discharged and there is a need for energy, it can be taken from another pack of batteries. The same analysis applies to the charge; when batteries of the first pack are fully charged and there is a surplus of energy, it can be stored in other packs of batteries. Two simulation results are used to demonstrate the efficiency of the EMS control used. These simulation tests are proposed with and without SCs. Full article

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article

With the high-proportion integration of renewable energy into the power grid, the fast-response capabilities of demand-side flexible resources (DSFRs), such as electric vehicles (EVs) and thermostatic loads, have become critical for frequency stability. However, the diverse dynamic characteristics of heterogeneous resources lead to high modeling complexity. Traditional reinforcement learning methods, which rely on neural networks to approximate value functions, often suffer from training instability and lack the effective quantification of resource regulation costs. To address these challenges, this paper proposes a multi-agent reinforcement learning frequency control method based on a Consistency Model (CM). This model incorporates power, energy, and first-order inertia characteristics to uniformly characterize the response delays and dynamic behaviors of EVs and air conditioners (ACs), providing a reduced-order analytical foundation for large-scale coordinated control. On this basis, a policy gradient controller is designed. By using projected gradient descent, it ensures that control actions satisfy physical boundaries. A reward function including state deviation penalties and regulation costs is constructed, dynamically adjusting penalty factors according to resource states to achieve priority configuration for frequency regulation. Simulations on the IEEE 39-node system demonstrate that the proposed method significantly outperforms traditional approaches in terms of frequency deviation, algorithm training efficiency, and frequency regulation economy. Full article

The question generation system (QGS) for information technology (IT) education, designed to create, evaluate, and improve Multiple-Choice Questions (MCQs) using knowledge graphs (KGs) and large language models (LLMs), encounters three major needs: ensuring the generation of contextually relevant and accurate distractors, enhancing the diversity of generated questions, and balancing the higher-order thinking of questions to match various learning levels. To address these needs, we proposed a multi-agent system named Multi-Examiner, which integrates KGs, domain-specific search tools, and local knowledge bases, categorized according to Bloom’s taxonomy, to enhance the contextual relevance, diversity, and higher-order thinking of automatically generated information technology MCQs. Our methodology employed a mixed-methods approach combining system development with experimental evaluation. We first constructed a specialized architecture combining knowledge graphs with LLMs, then implemented a comparative study generating questions across six knowledge points from K-12 Computer Science Standard. We designed a multidimensional evaluation rubric to assess the semantic coherence, answer correctness, question validity, distractor relevance, question diversity, and higher-order thinking, and conducted a statistical analysis of ratings provided by 30 high school IT teachers. Results showed statistically significant improvements (p < 0.01) with Multi-Examiner outperforming GPT-4 by an average of 0.87 points (on a 5-point scale) for evaluation-level questions and 1.12 points for creation-level questions. The results demonstrated that: (i) overall, questions generated by the Multi-Examiner system outperformed those generated by GPT-4 across all dimensions and closely matched the quality of human-crafted questions in several dimensions; (ii) domain-specific search tools significantly enhanced the diversity of questions generated by Multi-Examiner; and (iii) GPT-4 generated better questions for knowledge points at the “remembering” and “understanding” levels, while Multi-Examiner significantly improved the higher-order thinking of questions for the “evaluating” and “creating” levels. This study contributes to the growing body of research on AI-supported educational assessment by demonstrating how specialized knowledge structures can enhance automated generation of higher-order thinking questions beyond what general-purpose language models can achieve. Full article

In this paper, the fixed-time control for high-order nonlinear multi-agent systems under unknown stochastic time delay is investigated via an event-triggered approach. First of all, RBF neural networks are utilized to approximate the system’s uncertain nonlinearities. After that, an event-triggered scheme, which is designed with a relative threshold for more flexible control, is proposed to alleviate the communication burden. In consideration of the unknown stochastic time delay in the inter-communication among high-order nonlinear multi-agent systems, the Lyapunov–Krasovskii functional (LKF) is used to construct the system’s Lyapunov function, specifically targeting the adverse effects caused by time delay. Further, the fixed-time stability theory is employed to ensure that the convergence time remains independent of the initial values. Finally, the proposed control strategy is validated through numerical simulations. Full article

Although power sharing in hybrid AC/DC microgrids (HMGs) has been widely researched, traditional power-sharing control is based on an infinite time consensus method, and the communication bandwidth is large. Therefore, this paper proposes a power-sharing strategy for HMG parallel bi-directional interconnected converters (BICs) considering fixed-time stabilization and event-triggered control. Firstly, every BIC has a well-designed local control method to generate the corresponding power reference for the BIC, which provides the basis for further research. Secondly, a fixed-time-based power-sharing controller is designed in order to improve the convergence speed of power-sharing control for HMG parallel BICs. Finally, an event-triggered method is applied to reduce the system communication bandwidth and the frequency of controller updates. In this paper, we first transform the parallel BIC control problem into a multi-agent system (MAS) consensus problem. Furthermore, a fixed time based on an event trigger consensus method is proposed at the secondary control level. The energy flow between the two subgrids can be shared according to the rated power of each BIC. Finally, the effectiveness of the proposed fixed-time event-triggered power-sharing control is verified through simulation and experiments. Full article

This study addresses the completely distributed consensus control problem for the heterogeneous nonlinear multi-agent system (MAS) with disturbances under switching topology. First, a global sliding mode manifold (GSMM) is designed for the overall MAS dynamic, which maintains stability without oscillations during topology switching after achieving the sliding mode. Subsequently, a consensus sliding mode control protocol (SMCP) is proposed, adopting the common sliding mode control (SMC) format and ensuring the finite-time reachability of the GSMM under topology switching. Finally, the proposed GSMM and SMCP are applied to the formation control of multiple-wheeled mobile robots (WMRs), and simulation results confirm their feasibility and effectiveness. The proposed SMCP design demonstrates key advantages, including a simple control structure, complete robustness to matched disturbance, and reduced-order dynamics under the sliding mode. Full article

With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement. Full article

The problem of finite-time partial component consensus (FTPCC) for first-order nonlinear multi-agent systems (MASs) is investigated in this paper for the first time. By incorporating the permutation matrix approach, we derive a novel error system for identical components, which facilitates stability analysis. Leveraging partial variable stability theory and related foundational knowledge, we devise two adaptable protocols. These protocols are tailored to achieve FTPCC in nonlinear MASs, one for systems without disturbances and another for those with bounded disturbances. To validate our findings, numerical examples are provided, demonstrating the effectiveness of the proposed results. Full article

The consensus problem of a multi-agent system with nonlinear second-order underactuated agents is addressed. The essence of the approach can be outlined as follows: the output is redesigned first so that the agents attain the minimum-phase property. The second step is to apply the exact feedback linearization to the agents. This transformation divides their dynamics into a linear observable part and a non-observable part. It is shown that consensus of the linearizable parts of the agents implies consensus of the entire multi-agent system. To achieve the consensus of the original system, the inverse transformation of the exact feedback linearization is applied. However, its application causes changes in the dynamics of the multi-agent system; a way to mitigate this effect is proposed. Two examples are presented to illustrate the efficiency of the proposed synchronization algorithm. These examples demonstrate that the synchronization error decreases faster when the proposed method is applied. This holds not only for the states constituting the linearizable dynamics but also for the hidden internal dynamics. Full article

Finite-time consensus problem of hybrid-order heterogeneous multi-agent systems under a signed digraph topology is investigated in this paper. For heterogeneous multi-agent systems composed of first-order and second-order agents, a novel pinning event-triggered control protocol is devised to facilitate the attainment of the desired consensus state within a finite time. This control method overcomes communication barriers between first-order and second-order multi-agent systems, achieving effective control performance while reducing controller update frequency and communication costs. Based on graph theory and the Lyapunov stability method, several novel matrices are defined to address the finite-time consensus problem in hybrid-order multi-agent systems, and these matrices also facilitate the theoretical derivation process. Furthermore, it is demonstrated that the control protocol designed for hybrid-order systems is devoid of Zeno behavior. Finally, a detailed numerical example is supplied to illustrate the validity of the theoretical analysis. Full article