Multi-Agent Control: A Graph-Theoretic Perspective

Chen, Fei; Ren, Wei

doi:10.1007/s11424-021-1218-6

Cited by 18 publications

(11 citation statements)

References 207 publications

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“…The approximation c is obtained by fitting c with the following truncated Fourier series. The parameter s is defined as the normalized arc length of the observed curve (1), and it satisfies:…”

Section: B Curve Approximationmentioning

confidence: 99%

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Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Zhang¹,

Zhi²,

Romero³

et al. 2023

Preprint

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<p>The automatic monitoring/tracking of environmental boundaries by multi-agent systems is a fundamental problem which has many practical applications. In this paper, we address this problem with formation control techniques based on parametric curves that represent the boundary’s feedback shape. For that, we approximate the curve with truncated Fourier series, whose finite coefficients are utilized to characterize the curve’s shape and to automatically distribute the agents along it. These feedback Fourier coefficients are exploited to design a new type of formation controller that drives the agents to form desired curves. A detailed stability analysis is provided for the proposed control methodology, considering both fixed and switching multi-agent topologies. The reported numerical simulation study demonstrates the performance and feasibility of our new method to track closed boundaries of different shapes.</p>

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Section: B Curve Approximationmentioning

confidence: 99%

“…T HE control of multi-agent systems (MAS) is a challenging problem on which many researchers have made multiple fundamental contributions over the last few decades [1], [2]. Traditional control problems in MAS include consensus, formation control, task allocation, synchronization, etc.…”

Section: Introductionmentioning

confidence: 99%

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Zhang¹,

Zhi²,

Romero³

et al. 2023

Preprint

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“…Graph theory was applied for defining the structural controllability of multi-agent systems [234]. One of the most frequently used techniques in this domain is to use a graph Laplacian matrix to investigate MAS dynamics and convergence rates [67].…”

Section: Control Techniquesmentioning

confidence: 99%

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Nezamoddini

Gholami

2022

Smart Cities

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The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

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“…Based on graph theory, MASs can achieve distributed communication by utilizing the state information and connectivity between corresponding agents and their neighbors. Distributed topology structures have advantages such as easy scalability and high fault tolerance, which have attracted extensive research [4][5][6][7][8]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Self-Triggered Control for Multi-Agent Systems with Actuator Failures and Time-Varying State Constraints

Wang,

Hu,

Liu

et al. 2023

Actuators

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This work focuses on the consensus problem for multi-agent systems (MASs) with actuator failures and time-varying state constraints, and presents a fixed-time self-triggered consensus control protocol. The use of time-varying asymmetrical barrier Lyapunov functions (BLF) avoids the violation of time-varying state constraints in MASs, ensuring stability and safety. Meanwhile, the system’s performance is further enhanced by leveraging the proposed adaptive neural networks (NNs) control method to mitigate the effects of actuator failures and nonlinear disturbances. Moreover, a self-triggered mechanism based on a fixed-time strategy is proposed to reach rapid convergence and conserve bandwidth resources in MASs. The mechanism achieves consensus within a predefined fixed time, irrespective of the system’s initial states, while conserving communication resources. Finally, the proposed method’s effectiveness is confirmed through two simulation examples, encompassing diverse actuator failure scenarios.

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Multi-Agent Control: A Graph-Theoretic Perspective

Cited by 18 publications

References 207 publications

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Adaptive Self-Triggered Control for Multi-Agent Systems with Actuator Failures and Time-Varying State Constraints

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