This paper proposes a distributed Nash equilibrium seeking strategy for noncooperative games over strongly connected topologies, where the players suffer from unmodeled nonlinearities and external disturbances. By using the neural network (NN) universal approximation theorem, a neuro-adaptive seeking controller is proposed. Besides, the event-triggered mechanism is introduced to reduce communication costs of the players. Furthermore, by developing a Lyapunov function, it is shown that the states of the players asymptotically converge to the Nash equilibrium. Finally, a simulation is conducted to show the effectiveness of the proposed methods.

中文翻译：

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非线性多智能体系统中非合作博弈的分布式纳什均衡：一种事件触发的神经自适应方法

本文提出了一种分布式纳什均衡寻求策略，用于强连接拓扑上的非合作博弈，其中参与者遭受未建模的非线性和外部干扰。利用神经网络(NN)通用逼近定理，提出了一种神经自适应搜索控制器。此外，还引入了事件触发机制，降低了玩家的沟通成本。此外，通过开发李雅普诺夫函数，表明参与者的状态渐近收敛到纳什均衡。最后，通过仿真验证了所提方法的有效性。