This paper presents two decomposition approaches, Bilevel Optimization and Benders Decomposition, to a model predictive control of resource-constrained dynamic systems. The proposed methods yield a distributed solution that converges to the same optimum that would be obtained by a centralized controller. In this context, it is shown that the decompositions enable the use of multi-core or distributed architectures. A level regularization method is applied to accelerate the convergence of the Benders decomposition. Computational analyses from experiments with synthetic problems and a problem regarding the charging of vehicle batteries are reported and discussed, which showed that the decomposition approaches are effective at solving the distributed problems, achieving a global optimum.

中文翻译：

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资源受限动态系统模型预测控制分解策略的计算分析

本文针对资源受限的动态系统的模型预测控制，提出了两种分解方法：双层优化和Benders分解。所提出的方法产生了一种分布式解决方案，该解决方案收敛于由中央控制器获得的最优值。在这种情况下，证明了分解可以使用多核或分布式体系结构。应用级别正则化方法来加速Benders分解的收敛。报告和讨论了来自合成问题和汽车电池充电问题的实验的计算分析，结果表明，分解方法可有效解决分布式问题，实现全局最优。