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In this paper, a distributed model predictive control (DMPC) strategy is proposed based on agent coordination, in which subsystems couple through the inputs. At first, the initial feasible solution of each agent can be achieved by solving local optimization problems in which the state constraints of neighbor subsystems are considered at each sampling time. And then the global optimal solution can be obtained through agent coordination. In the negotiating process, the innovative global optimization objective is determined for the sake of reducing iteration time and improving the convergence speed efficiently. Finally, the accuracy and efficiency of the proposed scheme is put to test through simulation.