A class of large-scale systems, which is naturally divided into many smaller interacting subsystems, is usually controlled by a distributed or decentralized control framework. In this case, how to improve the performance of the entire system is a problem. A novel distributed model predictive control (MPC) is proposed for improving the global performance. Each subsystem is controlled by a subsystem-based MPC. These controllers coordinate with each other through global performance optimization index, and take the interactions among subsystems into account when predicting states evolution. The stability analysis for the unconstrained distributed MPC is given for guiding the control parameters tuning. Numeric results show that the proposed architecture could guarantee satisfactory global performance under even strong interactions among subsystems.