To decrease the overlarge calculation induced by the centralized processing,a new cooperative distributed Model predictive control(MPC)method is proposed for large-scale systems with coupled dynamics.Reduction and classification are investigated by defining the influence degree to reduce the whole system and then to classify the reduced system into several subsystem groups.These groups are mutually decoupled,while there is relativity between these subsystems comprised in the same group.Centralized/cooperative and distributed MPC algorithms for each group are implemented to ensure the feasibility and the stability of the whole system.Meanwhile,for practical applications,the finite times interactive control strategy between different groups is adopted to compensate information loss brought by the reduced subsystem and realize the global cooperative distributed MPC.This algorithm significantly decreases the computational load,has better control performance.Simulations are given to illustrate the effectiveness of these developed algorithms. To decrease the overlarge calculation induced by the centralized processing, a new cooperative distributed model predictive control (MPC) method is proposed for large-scale systems with coupled dynamics. Reduction and classification are investigated by defining the influence degree to reduce the whole system and then to classify the reduced system into several subsystem groups. These groups are mutually decoupled, while there is relativity between these subsystems comprised in the same group. Centralized / cooperative and distributed MPC algorithms for each group are implemented to ensure the feasibility and the stability of the whole system.Meanwhile, for practical applications, the finite times interactive control strategy between different groups is adopted to compensate information loss brought by the reduced subsystem and realize the global cooperative distributed MPC. This algorithm significant decreases the computational load, has better control performance. Simulations are given to illustr ate the effectiveness of these developed algorithms.