Distributed robust stochastic learning in asynchronous networks of sampled-data systems
Jorge I. Poveda, Andrew R. Teel · 2016
This paper presents a stochastic distributed algorithm for robust learning in networks of asynchronous sampled-data systems characterized by strongly connected directed graphs, where the response map of each sampled-data system has a quadratic structure, and the interactions between systems describe a Nash game. It is assumed that each sampled-data system has an individual resetting clock, as well as plant dynamics modeled by a differential inclusion. In order to achieve convergence in a practical mean-square sense to the Nash equilibrium of the game, we propose a robust distributed stochastic hybrid algorithm that coordinates and synchronizes the actions of the agents by using only local information from the network. A numerical example illustrates the results.