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Distributed model predictive control for continuous‐time nonlinear systems based on suboptimal ADMM
Author(s) -
Bestler Anja,
Graichen Knut
Publication year - 2018
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2459
Subject(s) - convergence (economics) , bounded function , scheme (mathematics) , nonlinear system , model predictive control , stability (learning theory) , mathematical optimization , computer science , control theory (sociology) , control (management) , mathematics , artificial intelligence , quantum mechanics , machine learning , mathematical analysis , physics , economics , economic growth
Summary This paper presents a distributed model predictive control (DMPC) scheme for continuous‐time nonlinear systems based on the alternating direction method of multipliers (ADMM). A stopping criterion in the ADMM algorithm limits the iterations and therefore the required communication effort during the DMPC solution at the expense of a suboptimal solution. Stability results are presented for the suboptimal DMPC scheme under two different ADMM convergence assumptions. In particular, it is shown that the required iterations in each ADMM step are bounded, which is also confirmed in simulation studies.