We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.
@article{arxiv.0802.4450,
title = {A Study On Distributed Model Predictive Consensus},
author = {Tamas Keviczky and Karl Henrik Johansson},
journal= {arXiv preprint arXiv:0802.4450},
year = {2008}
}
Comments
20 pages, 4 figures, longer version of paper presented at 17th IFAC World Congress