ADMM-based distributed model predictive control: Primal and dual approaches
ADMM-based distributed model predictive control: Primal and dual approaches
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DOI：
10.1109/cdc.2017.8264654
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Ramin Rostami;Giuliano Costantini;D. Görges
中科院分区：
文献类型：
--
作者：
Ramin Rostami;Giuliano Costantini;D. Görges
Distributed model predictive control (DMPC) is a widely used approach especially for large-scale systems like transportation, traffic control or power distribution systems where solving the MPC problem in a centralized manner is not desirable or even not possible due to the size of the problem, maintenance issues, cabling burden, etc. Distributed optimization methods, which are being employed to solve the DMPC problems, normally suffer from having an enormous number of communications to reach a consensus between the local controllers. Therefore trying to find distributed optimization schemes that can realize an agreement among the local controllers with a lower communication load deserves attention. The alternating direction method of multipliers (ADMM) is a well known method to solve consensus problems. Since the local problems in ADMM can be handled with second-order methods, the convergence will be reached with a lower number of iterations in comparison to first-order methods, e.g. the fast gradient method. In this paper, ADMM is formulated and applied to a DMPC problem first in the primal domain. The main contribution of this paper is, however, the application of ADMM to the dual of the DMPC problem, introducing an augmented connection graph, which shows considerable improvement in convergence speed and therefore requires a lower number of communications in comparison with primal ADMM.
