ADMM for Exploiting Structure in MPC Problems

• 成果类型: Article
• 署名作者: Rey, Felix; Hokayem, Peter; Lygeros, John
• 署名单位: Boston Consulting Group (BCG); Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3022492
• 发表日期: 2021
• 页码: 2076-2086
• 关键词: Alternating direction method of multipliers (ADMM); distribution; Predictive control; system structure exploitation

摘要

We consider a model predictive control setting, where we use the alternating direction method of multipliers (ADMM) to exploit 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: its computational complexity scales favorably with the problem size; it is highly parallelizable; it is highly adaptable to the problem at hand; and 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.