Distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM
Optimization and Control
2017-06-30 v1
Abstract
The 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 distributed MPC 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.
Cite
@article{arxiv.1706.09599,
title = {Distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM},
author = {Anja Bestler and Knut Graichen},
journal= {arXiv preprint arXiv:1706.09599},
year = {2017}
}
Comments
26 pages, 7 figures