发表机构
University of California, San Diego（加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于纳什均衡的SDP模型，为线性多智能体系统综合静态输出反馈控制，保证稳定性与耗散性，并通过IEEE 24节点电网案例验证其可扩展性与实用性。
AI 中文摘要
本文提出了一种半定规划（SDP）模型，用于综合网络化线性时不变多智能体系统（MAS）的静态输出反馈控制。我们从理论上证明，多智能体系统的输出反馈控制可以利用先前针对状态反馈控制推导出的纳什均衡来构造。纳什均衡考虑的是，智能体在受多智能体系统动态约束的情况下，力求最小化各自的代价函数。所提出的SDP模型产生的控制能够确保闭环系统的稳定性，并且当多智能体系统中存在扰动时，还能保证耗散性。本文实施了四组案例研究。前三个数值案例用于验证所提出的控制框架与其他最新框架在成本效益和时域性能方面的对比。第四个案例为改进的IEEE 24节点电网设计了频率调节方案。利用所提出的框架，为电网中被视为智能体的逆变器综合了分布式控制器。每个逆变器使用相邻逆变器的部分状态信息。所得控制方案在一个描述电网电磁暂态动态的、包含261个状态的高保真非线性模型中实施。因此，与以往通常在小型数值模型中验证其框架的研究相比，第四个案例展示了我们提出的SDP的一个优点：其可扩展性及在现实世界网络化多智能体系统中的实用性。我们将所提出的框架实现在一个开源的Python和MATLAB工具箱中，使从业者和研究人员能够在其应用中轻松实现。
英文摘要
This work proposes a semidefinite programming (SDP) model that synthesizes static output control feedback for networked linear time-invariant multi-agent systems (MAS). We theoretically prove that the output feedback control of MAS can be constructed using the Nash equilibrium previously derived for state-feedback control. The Nash equilibrium considers that agents seek to minimize their individual cost functions subject to the system dynamics of the MAS. The resulting control from the pro- posed SDP model ensures the stability of the closed-loop system and dissipativity when a disturbance is present in the MAS. A set of four case studies is implemented. The first three numerical cases serve to validate and compare cost effectiveness and time-domain performance of the proposed control framework with other state-of-the-art frameworks. The fourth case designs a frequency regulation scheme for a modified IEEE 24-bus power grid. Using the proposed framework, distributed controllers are synthesized for the inverters which are considered agents in the grid. Each inverter uses partial state information of the neighboring inverters. The resulting control scheme is implemented in a high-fidelity non-linear model of 261 states that describes the electromagnetic transient dynamics of the power grid. Hence, compared to prior studies that usually validate their frameworks in small-scale numerical models, this fourth case demonstrates a virtue of our pro- posed SDP: its scalability and practical utility in real-world networked MAS. We implement the proposed framework in an open-access toolbox in Python and MATLAB, enabling practitioners and researchers to readily implement it in their applications.
