  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.
