  In this paper, we present a robust distributed model predictive control
(DMPC) scheme for dynamically decoupled nonlinear systems which are subject to
state constraints, coupled state constraints and input constraints. In the
proposed control scheme, all subsystems solve their local optimization problem
in parallel and neighbor-to-neighbor communication suffices. The approach
relies on consistency constraints which define a neighborhood around each
subsystem's reference trajectory where the state of the subsystem is guaranteed
to stay in. Reference trajectories and consistency constraints are known to
neighboring subsystems. Contrary to other relevant approaches, the reference
trajectories are improved consecutively. The presented approach allows the
formulation of convex optimization problems for systems with linear dynamics
even in the presence of non-convex state constraints. Additionally, we employ
tubes in order to ensure the controller's robustness against bounded
uncertainties. In the end, we briefly comment on an iterative extension of the
DMPC scheme. The effectiveness of the proposed DMPC scheme and its iterative
extension are demonstrated with simulations.
