基于复拉普拉斯的多Agent网络分布式控制

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摘要翻译：
本文所做的工作，提出了一种基于复杂拉普拉斯的分布式多智能体网络收敛控制方案。提出的方案被指定为级联方案。该技术利用传统的将大型分散网络组织成更小的相互连接的集群的方法来优化网络中的信息流。复杂的基于拉普拉斯的方法导致了一个层次结构，形成了一个元簇，领先于网络中的其他簇。所提出的公式能够灵活地约束整个闭环动力学的本征谱，保证期望的收敛速度和控制输入强度。并给出了所提出的公式的全局稳定形成的充分条件。文中还讨论了该公式对通信链路丢失和执行器故障等不确定性的鲁棒性。通过对一个由30辆车组成的有限大网络的仿真，说明了该方法的有效性。
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英文标题：
《Complex Laplacian based Distributed Control for Multi-Agent Network》
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作者：
Aniket Deshpande, Pushpak Jagtap, Prashant Bansode, Arun Mahindrakar,
  Navadeep Singh
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最新提交年份：
2018
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分类信息：

一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Systems and Control        系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
  The work done in this paper, proposes a complex Laplacian-based distributed control scheme for convergence in the multi-agent network. The proposed scheme has been designated as cascade formulation. The proposed technique exploits the traditional method of organizing large scattered networks into smaller interconnected clusters to optimize information flow within the network. The complex Laplacian-based approach results in a hierarchical structure, with formation of a meta-cluster leading other clusters in the network. The proposed formulation enables flexibility to constrain the eigen spectra of the overall closed-loop dynamics, ensuring desired convergence rate and control input intensity. The sufficient conditions ensuring globally stable formation for proposed formulation are also asserted. Robustness of the proposed formulation to uncertainties like loss in communication links and actuator failure has also been discussed. The effectiveness of the proposed approach is illustrated by simulating a finitely large network of thirty vehicles.
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PDF链接：
https://arxiv.org/pdf/1609.0552

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关键词：agent 拉普拉斯 分布式 Age 普拉斯 方法 方案 formulation 故障 输入