Neural Network-based Distributed Generalized Nash Equilibrium Seeking for Uncertain Nonlinear Multi-agent Systems

IEEE Transactions on Control of Network Systems(2023)

引用 0|浏览0
暂无评分
摘要
This paper investigates distributed variational generalized Nash equilibrium (v-GNE) seeking problems in heterogeneous high-order nonlinear multi-agent systems with partially unknown dynamics. To overcome the difficulties brought by high-order uncertain physical dynamics, we introduce a virtual decision with a primal-dual update for each player and design reference-tracking schemes to guide the players' outputs toward the v-GNE. Besides, we incorporate a shallow neural network (NN)-based dynamic estimator to handle the unknown nonlinear dynamics. Through Lyapunov analysis, we demonstrate that the players' behaviors converge to the v-GNE with an arbitrarily small error. Numerical simulations of connectivity control games and energy consumption games illustrate the effectiveness of our algorithm.
更多
查看译文
关键词
Multi-agent systems,generalized Nash equilibrium,neural network,adaptive control
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要