An adaptive neurodynamic approach for solving nonsmooth N-cluster games.

In this paper, N-cluster games with coupling and private constraints are studied, where each player's cost function is nonsmooth and depends on the actions of all players. In order to seek the generalized Nash equilibrium (GNE) of the nonsmooth N-cluster games, a distributed seeking neurodynamic approach with two-time-scale structure is proposed. An adaptive leader-following consensus technique is adapted to dynamically adjust parameters according to the degree of consensus violation, so as to quickly obtain accurate estimation information of other players' actions which facilitates the evaluation of its own cost. Benefitting from the unique structure of the approach based on primal dual and adaptive penalty methods, the players' actions enter the constraints while completing the seeking for GNE. As a result, the neurodynamic approach is completely distributed, and prior estimation of penalty parameters is avoided. Finally, two engineering examples of power system game and company capacity allocation verify the effectiveness and feasibility of the neurodynamic approach.