Multi-agent Research Articles

Prescribed finite-time adaptive tracking control for a class of full state constrained non-strict feedback nonlinear multi-agent systems

Prescribed finite-time adaptive tracking control for a class of full state constrained non-strict feedback nonlinear multi-agent systems

Distributed Adaptive Formation Control for Fractional-Order Multi-Agent Systems with Actuator Failures and Switching Topologies

In this paper, a class of distributed adaptive formation control problems are investigated for second-order nonlinear fractional-order multi-agent systems with actuator failures and switching topologies. To address these challenges, two adaptive coupling gains based on agents’ position and velocity are incorporated into the control protocol. Using the Lyapunov method along with graph theory and matrix analysis, sufficient conditions for system stability are derived in the presence of actuator failures and switching topologies. The effectiveness of the proposed control protocol is demonstrated through numerical simulations, which show its capability to maintain stable formation control under these challenging conditions.

Dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for nonlinear multi-agent systems

This paper is concerned with the problem of dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for multi-agent systems subject to actuator saturation, in which state variables are unmeasurable and nonlinear functions are totally unknown. A fuzzy state observer is constructed to estimate unmeasurable states. Meanwhile, fuzzy logic systems are used to approximate unknown nonlinear functions. To effectively save the usage of communication resources, this paper designs both sensor output signal and control signal triggering mechanisms, respectively. Unfortunately, the output triggering can lead to a problem that virtual control laws are non-differentiable. To solve this problem, we first utilize observer output signals to construct virtual control laws to ensure the first-order differentiation of virtual control laws. Then, a dynamic filtering technology is introduced to avoid the repeated differentiation of virtual control laws. Furthermore, an improved first-order auxiliary system is designed to compensate for the impact of actuator saturation. It is shown that the designed controller can guarantee tracking errors steer to a preset accuracy within a prescribed settling time, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results verify the effectiveness of the developed control scheme.

Multi-agent system-based framework for an intelligent management of competency building

To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background and skills of each learner, individual learning styles have been identified and measured by several researchers. Despite their importance in personalizing the learning activities, these styles are difficult to implement for large groups of learners. They have also been rarely correlated with each specific learning stage. New approaches are, therefore, needed to intelligently coordinate all the learning activities while self-adapting to the ongoing progress of learning as well as to the specific requirements and backgrounds of learners. To address these issues, we propose in this paper a new framework for an intelligent management of the competency building process during learning. Our framework is based on a recursive spiral Assess-Predict-Oversee-Transit model that is orchestrated by a multi-agent system. This system is particularly responsible of enabling smart transitions between learning stages. It is also responsible of assessing and predicting the process of competency building of the learner and, then, making the right decisions about the learning progress, accordingly. Results of our solution were demonstrated via an Augmented Reality app that we created using the Unity3D engine to train learners on Air Conditioner maintenance.

Fixed-Time Adaptive Fuzzy Control for Uncertain Multi-agent Systems with Deferred Dynamic Constraints via Dynamic-Memory Event-Triggered Mechanism

Fixed-Time Adaptive Fuzzy Control for Uncertain Multi-agent Systems with Deferred Dynamic Constraints via Dynamic-Memory Event-Triggered Mechanism

Reachable set control of singular semi-Markov jump multi-agent systems with input delay via state decomposition method

This paper addresses the challenge of reachable set estimation in singular multi-agent systems with time-delay and semi-Markov switching topologies. The primary goal is to construct an ellipsoid that encompasses all states originating from the origin. Furthermore, criteria for reachable sets with reduced conservatism are developed. The singular multi-agent systems are reduced by the Laplace matrix, then the Lyapunov–Krasovskii functional is constructed by using decomposed state vector. In addition, the sufficient conditions for the reachable set of singular multi-agent systems bounded by ellipsoids are given by matrix theory and linear matrix inequality. Numerical examples show that our results is effective.

Containment control for non-linear fractional-order multi-agent systems via refined sample data controller

Abstract This manuscript concentrates on the problem of designing a sampled data controller (SDC) for the consensus of a fractional-order multi-agent system (FOMAS) with Lipschitz non-linearity via an algebraic approach. The solution of the FOMAS is represented by using the Laplace transform approach. An upper bound of the sampling period is determined through various integral inequality techniques. Distinguished from the existing works, the estimate for an upper bound is more accurate which involves the Lipschitz constant of the non-linear function. Finally, numerical examples are given to validate the correctness of results. Furthermore, the comparison results are presented to show the proposed method determines a better upper bound of the sampling period.

Reinforcement learning intermittent optimal formation control for multi-agent systems with disturbances

Abstract This paper investigates disturbance-resistant intermittent event-triggered optimal formation control problems of second-order multi-agent systems by using the reinforcement learning method, which takes into account the influence of network damage including denial-of-service (DoS) and deception attacks, stochastic noises, and unknown external disturbances. Firstly, we propose a novel disturbance observer based on adaptive control to estimate unknown external disturbances under an event-triggered mechanism. Secondly, by use of estimation of disturbances, an innovative intermittent event-triggered optimal formation algorithm is given. By applying theories such as Lyapunov stability and stochastic stability, sufficient conditions are derived to guarantee that all agents achieve the desired formation in mean square sense. Additionally, in the model-free case, the optimal controller is solved using the least squares method, which is computationally less complex than some existing approaches. Finally, the theoretical results are effectively validated through simulation examples.

Personalized Education via Mobile Technology Flexible Learning Frameworks

The prevalence of mobile and ubiquitous computing equipment is transforming how learners conduct their education. Nevertheless, the bulk of educational content generated for desktop platforms is unacceptable for handheld gadgets. Furthermore, some materials that are irrelevant to the preferences of the learner or the contextual environment might reduce the effect of learning and elevate the cost of communication. Furthermore, there is a deficit in the academic structure for mobile learning. This paper proposes personalized adaptation techniques for customized modification contents. This in turn strives to offer adaptive content contingent on learner experience and device capabilities. On top of that, it recommends a few alternatives to producing intelligent and adaptive material for children. Numerous facets of the study are examined to employ the benefits of learner, device, and location modeling in tandem with mobile technology to provide a personalized spread of multimedia-rich resources at any time or anywhere. As an outcome, it delivers proper learning resources and applies the learning style most suitable to the demands and tastes of each learner (personalized learning). Our system enables several types of learning styles to pick from, but the most significant aspect is to pair each learner’s characteristics with the right program and learning style. A novel chance for a broad range of learners to boost their knowledge in a tailored manner is made accessible by the adaptation of the material and styles, which enables the learning process to be dynamic. Additionally, our system’s resilience and interaction have risen owing to multi-agent system modeling.

Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A Multi-agent System Approach in Karaj Plain, Iran

Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A Multi-agent System Approach in Karaj Plain, Iran

The Role of Multi-Agents in Digital Twin Implementation: Short Survey

In recent years, Digital Twin (DT) technology has emerged as a significant technological advancement. A digital twin is a digital representation of a physical asset that mirrors its data model, behaviour, and interactions with other physical assets. Digital Twin aims to achieve adaptability, seamless data integration, modelling, simulation, automation, and real-time data management. The primary goal of this paper is to explore the role of agents in DT implementations, seeking to understand their predominant usage scenarios and purposes. From our perspective, agents serving as intelligent entities play a role in realising the features of DTs. This paper also discusses the gaps in DT, highlights future directions, and analyses various technologies integrated with multi-agent systems technologies in DT implementations. Finally, the paper briefly discusses an overview of an architecture to implement a DT for smart agriculture with multi-agents.

Impulsive fault-tolerant adaptive control on second-order nonlinear multiagent systems by extended observer approach

Impulsive fault-tolerant adaptive control on second-order nonlinear multiagent systems by extended observer approach

Interactive multi-agent convolutional broad learning system for EEG emotion recognition

Electroencephalogram (EEG) emotion recognition is gaining significance in intelligent human–computer interaction. Multi-agent learning can capture more complete and reliable features, compensating for the lack of a single agent’s knowledge. However, previous EEG emotion recognition only focused on a single agent. Therefore, to extract effective features from high-dimensional EEG data, a novel interactive multi-agent (IMA) learning framework is proposed, and introduced into convolutional broad learning system (CNNBLS), then the interactive multi-agent convolutional broad learning system (IMA-CNNBLS) is proposed for EEG emotion recognition. It can effectively model high-dimensional EEG data, automatically extract EEG features related to emotions through convolutional neural network (CNN), and then extend the above features to a vast space to quickly extract generalized features through broad learning system (BLS), consider a CNNBLS as an agent, and multi-agent interact in the IMA part. As the theoretical basis of IMA-CNNBLS, the consistency is mathematically proved through the stationary distribution theory of Markov processes. To demonstrate the superiority of our proposed model, sufficient experiments are conducted on the DEAP, DREAMER and SEED datasets. The experimental results show the model can effectively improve the EEG emotion recognition accuracy, the best recognition performance is achieved on all datasets. In addition, our proposed model also shows the multi-agent interaction significantly affects EEG emotion recognition.

Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.

Control and real-time experiments for a multi-agent aerial transportation system

Control and real-time experiments for a multi-agent aerial transportation system

Reducing Bandwidth Usage in CVSLAM: A Novel Approach to Map Point Selection and Efficient Data Compression

In the field of collaborative visual simultaneous localization and mapping (CVSLAM), efficient data communication poses a significant challenge, particularly in environments with limited bandwidth. To address this issue, we introduce a method aimed at reducing communication consumption. Our approach starts with a strategic culling of map points, aiming at maximizing pose-visibility and expanding spatial diversity to effectively eliminate redundant data in CVSLAM. We achieve this by formulating the problem of maximizing pose-visibility and spatial diversity as a minimum-cost maximum-flow graph optimization problem. Subsequently, we apply finite state entropy encoding for the compression of visual information, further alleviating bandwidth constraints. To verify the proposed method, we implement it within a centralized collaborative monocular simultaneous localization and mapping (SLAM) system. Our approach has been tested on publicly available datasets and in real-world scene. The results show a prominent reduction in bandwidth usage by 49% while maintaining mapping accuracy and without introducing additional latency, confirming its effectiveness in a multi-agent system setting.

Adaptive output feedback time‐varying formation tracking of multi‐agent system with a leader of unknown input

AbstractThis paper researches the time‐varying formation tracking (TVFT) problem of linear multi‐agent systems (MASs). By designing a compensator, the problem of time‐varying formation can be considered as the output regulation problem. Thereby, the distributed output feedback controller combined with an adaptive technique is proposed. With this controller, follower agents achieve the desired time‐varying formation and follow the trajectory of the leader agent. Furthermore, extending the designed controller to the case where the leader agent equips with unknown control input. Using Lyapunov stability theory, it is demonstrated that under proper conditions the given protocol is implementable. Simulation example is presented at the end of the paper to illustrate the effectiveness of designed control mechanism.

Dynamic Event-Triggered Prescribed-Time Consensus Tracking of Nonlinear Time-Delay Multiagent Systems by Output Feedback

Dynamic Event-Triggered Prescribed-Time Consensus Tracking of Nonlinear Time-Delay Multiagent Systems by Output Feedback

Kinetic description and macroscopic limit of swarming dynamics with continuous leader–follower transitions

In this paper, we derive a kinetic description of swarming particle dynamics in an interacting multi-agent system featuring emerging leaders and followers. Agents are classically characterized by their position and velocity plus a continuous parameter quantifying their degree of leadership. The microscopic processes ruling the change of velocity and degree of leadership are independent, non-conservative and non-local in the physical space, so as to account for long-range interactions. Out of the kinetic description, we obtain then a macroscopic model under a hydrodynamic limit reminiscent of that used to tackle the hydrodynamics of weakly dissipative granular gases, thus relying in particular on a regime of small non-conservative and short-range interactions. Numerical simulations in one- and two-dimensional domains show that the limiting macroscopic model is consistent with the original particle dynamics and furthermore can reproduce classical emerging patterns typically observed in swarms.

Fully distributed practical fixed-time fault-tolerant consensus of nonlinear multiagent systems

In this article, the practical fixed-time fault-tolerant consensus problem of a class of first-order nonlinear multiagent systems with actuator faults is investigated. Owing to the existence of uncertain actuator failures (multiplicative faults and additive faults) and non-linearities, it is challenging to study the fixed-time consensus problem of such multiagent systems in a fully distributed approach. To get over this challenge, a fully distributed adaptive protocol that utilizes adaptive techniques is proposed. Under this control protocol, multiagent systems can achieve consensus, and by adjusting controller parameters, the consensus error can converge to the acceptable bounded region in a fixed time. In addition, due to the introduction of adaptive parameters, the design of the control protocol is not dependent on any global network topology information, which means the control protocol is fully distributed. Therefore, the proposed scheme has better scalability and wider applicability. Finally, the simulation results are proposed to validate the fault-tolerant ability and fully distributed characteristics of the presented scheme.