AI Article Synopsis
- The paper explores the problem of finding distributed Nash equilibria (NE) in aggregative games where players' dynamics are affected by uncertainties.
- Each player develops a distributed algorithm using their cost function along with information shared by neighboring players.
- The proposed solution combines internal model principles with average consensus techniques, ensuring players can achieve NE while effectively handling external disturbances.
Article Abstract
In this paper, we study the distributed Nash equilibrium (NE) seeking problem for a class of aggregative games with players described by uncertain perturbed nonlinear dynamics. To seek the NE, each player needs to construct a distributed algorithm based on information of its cost function and the exchanging information obtained from its neighbors. By combining the internal model principle and the average consensus technique, we propose a distributed gradient-based algorithm for the players. This paper not only assures the NE seeking of aggregative games but also achieves the disturbance rejection of external disturbances.
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| http://dx.doi.org/10.1109/TCYB.2019.2929394 | DOI Listing |
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