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In this paper, we present a robust distributed model predictive control(DMPC) scheme for dynamically decoupled nonlinear systems which are subject tostate constraints, coupled state constraints and input constraints. In theproposed control scheme, all subsystems solve their local optimization problemin parallel and neighbor-to-neighbor communication suffices. The approachrelies on consistency constraints which define a neighborhood around eachsubsystem's reference trajectory where the state of the subsystem is guaranteedto stay in. Reference trajectories and consistency constraints are known toneighboring subsystems. Contrary to other relevant approaches, the referencetrajectories are improved consecutively. The presented approach allows theformulation of convex optimization problems for systems with linear dynamicseven in the presence of non-convex state constraints. Additionally, we employtubes in order to ensure the controller's robustness against boundeduncertainties. In the end, we briefly comment on an iterative extension of theDMPC scheme. The effectiveness of the proposed DMPC scheme and its iterativeextension are demonstrated with simulations.

Parallelized Robust Distributed Model Predictive Control in the Presence of Coupled State Constraints