A dynamic real-time optimization (DRTO) formulation with closed-loop prediction is used to coordinate distributed model predictive controllers (MPCs) by rigorously predicting the interaction between the distributed MPCs and full plant response in the DRTO formulation. This results a multi-level optimization problem that is solved by replacing the MPC quadratic programming subproblems by their equivalent Karush-Kuhn-Tucker (KKT) first-order optimality conditions to yield a single-level mathematical program with complementarity constraints (MPCC). The proposed formulation is able to perform both target tracking and economic optimization with significant performance improvement over decentralized control, and similar performance to centralized MPC. A linear dynamic case study illustrates the performance of the proposed strategy for coordination of distributed MPCs for different levels of plant interaction. The method is thereafter applied to a nonlinear integrated plant with recycle, where its performance in both set-point target tracking and economic optimization is demonstrated.

具有闭环预测的动态实时优化（DRTO）公式用于通过严格预测DRTO公式中的分布式MPC与整个工厂响应之间的相互作用来协调分布式模型预测控制器（MPC）。这导致了一个多级优化问题，该问题可以通过用等效的Karush-Kuhn-Tucker（KKT）一阶最优条件替换MPC二次规划子问题来解决，以产生具有互补性约束（MPCC）的单级数学程序。所提出的公式能够执行目标跟踪和经济优化，并且与分散控制相比具有显着的性能改进，并且具有与集中式MPC相似的性能。线性动态案例研究说明了针对不同级别的植物交互作用，所建议的用于协调分布式MPC的策略的性能。此方法此后应用于带有回收的非线性综合工厂，并在设定点目标跟踪和经济优化方面证明了其性能。