论文标题
基于极端寻求控制的分布式广泛的NASH均衡寻求算法
A distributed generalized Nash equilibrium seeking algorithm based on extremum seeking control
论文作者
论文摘要
在本文中，提议将共同寻求极端寻求控制（ESC）与学习算法相结合的分布式非模型的寻求算法，以寻求对一类非合作游戏和耦合均等约束的广义NASH平衡（GNE）。每个代理的策略都受到耦合的代理间约束和局部不平等约束的限制。多亏了ESC，不必要知道代理商成本功能和本地限制的具体表达，并了解其他代理商在实施拟议的GNE寻求算法的策略中的策略。为了处理耦合约束，只有Lagrange乘法器才能在代理之间传输一些有关耦合约束的先前信息。此外，在寻求算法中设计了减少的抖动信号，以消除不良的稳态振荡。通过单数扰动理论，平均分析和Lyapunov稳定性理论分析了设计的寻求算法的非本地收敛。给出了数值示例以验证我们提出的方法的有效性。
In this paper, a distributed non-model based seeking algorithm which combines the extremum seeking control (ESC) jointly with learning algorithms is proposed to seek a generalized Nash equilibrium (GNE) for a class of noncooperative games with coupled equality constraint. The strategy of each agent is restricted by both the coupled inter-agent constraint and local inequality constraints. Thanks to the ESC, it is unnecessary to know the specific expressions of agents' cost functions and local constraints and to know the strategies of other agents for the implementation of the proposed GNE seeking algorithm. To deal with the coupled constraints, only the Lagrange multiplier is transmitted among agents with some prior information about the coupled constraints. Moreover, a diminishing dither signal is designed in the seeking algorithm to remove undesirable steady-state oscillations. The non-local convergence of the designed seeking algorithm is analyzed via the singular perturbation theory, averaging analysis and Lyapunov stability theory. Numerical examples are given to verify the effectiveness of our proposed method.
