基于控制障碍函数的安全分布式广义纳什均衡寻求
Safe Distributed Generalized Nash Equilibrium Seeking via Control Barrier Functions
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中文总结 AI 辅助
本文针对带耦合约束集的非合作博弈，将控制障碍函数（CBF）引入分布式广义纳什均衡（GNE）寻求动力学设计，验证了安全性与渐近稳定性，扩展至多积分器智能体并经仿真验证。
中文摘要 AI 辅助
本文研究具有耦合约束集的非合作博弈中的广义纳什均衡（GNE）寻求问题，旨在为分布式GNE寻求过程确保安全性，安全规范编码在耦合约束集中。为此，我们将控制障碍函数（CBF）引入GNE寻求动力学的设计中，分别针对全信息和部分信息场景设计动力学：全信息场景中每个参与者知晓所有其他参与者的决策信息，部分信息场景中每个参与者仅知晓相邻参与者的决策信息。我们通过证明耦合约束集是前向不变集、动力学的平衡点与博弈的精确GNE一致、动力学渐近稳定来验证所提动力学的合理性。此外，我们将该方法扩展到智能体为多积分器的博弈中，并通过数值仿真验证了所得结果。
英文摘要
In this paper, we consider generalized Nash equilibrium (GNE) seeking in non-cooperative games with coupled constraint sets. Specifically, we aim to enforce safety for distributed GNE seeking, whereby the safety specifications are encoded in the coupled constraint set. To achieve this, we introduce the control barrier function (CBF) in the design of the GNE seeking dynamics. We design the dynamics for both full- and partial-information setting, where each player has knowledge of the decision information of all other players or only neighboring players, respectively. We justify the proposed dynamics by showing that the coupled constraint set is forward invariant, the equilibrium of the dynamics coincides with the exact GNE of the game, and the dynamics is asymptotically stable. Furthermore, we extend the approach to games where the agents are multi-integrators. Numerical simulations are provided to verify our results.
发表机构
- University of Toronto（多伦多大学）
- University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。
