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ADMM for Exploiting Structure in MPC Problems
IEEE Transactions on Automatic Control ( IF 6.8 ) Pub Date : 2020-01-01 , DOI: 10.1109/tac.2020.3022492
Felix Rey , Peter Hokayem , John Lygeros

We focus on a model predictive control (MPC) setting, where we use the alternating direction method of multipliers (ADMM) for exploiting problem structure. We take advantage of interacting components in the controlled system by decomposing its dynamics with virtual subsystems and virtual inputs. We introduce subsystem-individual penalty parameters together with optimal selection techniques. Further, we propose a novel measure of system structure, which we call separation tendency. For a sufficiently structured system, the resulting structure-exploiting method has the following characteristics: (i) its computational complexity scales favorably with the problem size; (ii) it is highly parallelizable; (iii) it is highly adaptable to the problem at hand; and (iv), even for a single-thread implementation, it improves the overall performance. We show a simulation study for cascade systems and compare the new method to conventional ADMM.

中文翻译:

用于在 MPC 问题中利用结构的 ADMM

我们专注于模型预测控制 (MPC) 设置,其中我们使用乘法器的交替方向方法 (ADMM) 来开发问题结构。我们通过使用虚拟子系统和虚拟输入分解其动态来利用受控系统中的交互组件。我们介绍了子系统个体惩罚参数和最优选择技术。此外,我们提出了一种新的系统结构度量,我们称之为分离趋势。对于一个足够结构化的系统,由此产生的结构开发方法具有以下特征:(i) 其计算复杂度与问题的大小成正比;(ii) 它是高度可并行化的;(iii) 对手头问题的适应性强;(iv) 即使对于单线程实现,它也提高了整体性能。
更新日期:2020-01-01
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