Distributed Robust Nash Equilibrium Seeking for Mixed-Order Games by a Neural-Network based Approach

TL;DR

This paper proposes a neural-network-based distributed algorithm for Nash equilibrium seeking in mixed-order games with unknown dynamics and disturbances, ensuring convergence to an approximate equilibrium.

Contribution

It introduces a novel adaptive neural network approach for distributed Nash equilibrium seeking in mixed-order games with unknown dynamics and disturbances.

Findings

Convergence to a neighborhood of Nash equilibrium is analytically proven.

Simulations confirm the effectiveness of the proposed method.

The approach manages heterogeneous dynamics in multi-agent decision-making.

Abstract

In practical applications, decision-makers with heterogeneous dynamics may be engaged in the same decision-making process. This motivates us to study distributed Nash equilibrium seeking for games in which players are mixed-order (first- and second-order) integrators influenced by unknown dynamics and external disturbances in this paper. To solve this problem, we employ an adaptive neural network to manage unknown dynamics and disturbances, based on which a distributed Nash equilibrium seeking algorithm is developed by further adapting concepts from gradient-based optimization and multi-agent consensus. By constructing appropriate Lyapunov functions, we analytically prove convergence of the reported method. Theoretical investigations suggest that players' actions would be steered to an arbitrarily small neighborhood of the Nash equilibrium, which is also testified by simulations.