Data-driven Subspace-based Distributed Control and Its Algorithmic Convergence

Abstract

This paper proposed a new data-driven subspace-based distributed control strategy based on Nash optimality. The distributed controller of each subsystem exchange the input-output information with other subsystems by networks. Communication among the controllers is helped to make each controller work in coordination with the others. In this way, the control performance of each subsystem is improved by considering the interactions among subsystems. An iteration algorithm is utilized to achieve the Nash equilibrium. The computational convergence of the algorithm is discussed. Simulations on a radial distribution power system network would provided to verify the validness of the proposed control strategy.