论文标题
通过基于神经网络的方法，分布式强大的NASH平衡寻求混合订单游戏
Distributed Robust Nash Equilibrium Seeking for Mixed-Order Games by a Neural-Network based Approach
论文作者
论文摘要
在实际应用中，具有异质动力的决策者可能会参与相同的决策过程。这激发了我们研究分布的NASH平衡，以寻求玩家在本文中受未知动态和外部干扰影响的游戏中的游戏（一阶和二阶）积分器。 为了解决这个问题，我们采用自适应神经网络来管理未知的动态和干扰，基于分布式NASH平衡寻求算法是通过从基于梯度的优化和多代理共识中进一步调整概念来开发的。通过构建适当的Lyapunov功能，我们在分析上证明了报告方法的收敛性。理论研究表明，玩家的行动将被转向纳什均衡的任意小社区，这也通过模拟作证。
In practical applications, decision-makers with heterogeneous dynamics may be engaged in the same decision-making process. This motivates us to study distributed Nash equilibrium seeking for games in which players are mixed-order (first- and second-order) integrators influenced by unknown dynamics and external disturbances in this paper. To solve this problem, we employ an adaptive neural network to manage unknown dynamics and disturbances, based on which a distributed Nash equilibrium seeking algorithm is developed by further adapting concepts from gradient-based optimization and multi-agent consensus. By constructing appropriate Lyapunov functions, we analytically prove convergence of the reported method. Theoretical investigations suggest that players' actions would be steered to an arbitrarily small neighborhood of the Nash equilibrium, which is also testified by simulations.
