N-Step Impacted-Region Optimization based Distributed Model Predictive Control

摘要

The Distributed Model Predictive Control (DMPC) has been more and more popular in the control of distributed systems which are composed by many interacted subsystems. The range of subsystems that each local Model Predictive Control (MPC) optimized, called coordination degree, plays an important role in improving the optimization performance of entire closed-loop system. In this paper, the N-step adjacent structure matrix based decomposition method was proposed, where the coordination degree of each subsystem is determined by the union of the all the adjacent matrices over the predictive horizon. Based on this decomposition, each local MPC considers the cost of all the subsystems it impacted on during the predictive horizon, and then improves the optimization performance of entire system with reduced communication burdens. The simulation results show the effectiveness of the proposed method.