AI Article Synopsis

  • The article explores how to find the distributed generalized Nash equilibrium (GNE) in noncooperative games involving complex multiagent systems with specific constraints.
  • It introduces a hierarchical approach to simplify the GNE seeking process by designing a two-layer algorithm: a primary-dual estimator for generating reference signals and an adaptive tracking controller to handle output constraints.
  • The proposed method addresses disturbances using compensating terms, ensuring the accurate attainment of GNE while demonstrating its effectiveness through a practical example.

Article Abstract

This article investigates the distributed generalized Nash equilibrium (GNE) seeking problem of noncooperative games (NGs) for high-order strict-feedback nonlinear multiagent systems (MASs). In particular, the feasible action set of each agent is not only subject to local set and inequality constraints but also coupled through an equality constraint with other agents. This constraint structure is more general and covers most of the constraints in the GNE seeking literature. To accomplish the concerned GNE seeking objective, we propose a novel hierarchical GNE seeking approach in this article to decouple the distributed GNE seeking algorithm design into two layers. First, we construct a distributed primary-dual GNE estimator to generate virtual reference signals that converge to the GNE. Then, with the output of the estimator as the reference signal, we develop an adaptive tracking controller to solve the resultant tracking problems under output constraints. To overcome the negative effects of the disturbances, novel compensating terms associated with smooth functions and positive integrable time-varying functions are incorporated in the controller design, which thereby realizes the exact GNE seeking in the presence of nonvanishing mismatched disturbances. At last, an example is given to support the theoretical analysis of the proposed algorithms.

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Source
http://dx.doi.org/10.1109/TCYB.2024.3418569DOI Listing

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