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Model predictive control (MPC) has become one of the leading technologies to control complex processes, to a great extent, as a result of its flexibility and explicit handling of constraints. Given a dynamic problem (DP), MPC converts DP into a series of static optimization problems, thereby allowing the use of standard optimization techniques to compute the control signals. The reliance of MPC on centralized computations, however, stands as a barrier to its use in the real-time operation of large dynamic networks. To this end, this paper proposes an extension to MPC by decomposing DP into a network of small but coupled subproblems and solving them with a network of asynchronous agents. The net result, after each agent applies MPC to its dynamic subproblem, is a series of sets of static subproblems. Our focus is on the simultaneous solution of these sets of static subproblems. The paper delivers a framework to carry out the decomposition and develops conditions under which the iterative synchronous processes of the agents converge to solutions. Furthermore, it proposes heuristics for asynchronous convergence and reports experimental results from prototypical dynamic networks, demonstrating the effectiveness of the proposed extension.