Set-based model predictive consensus under bounded additive disturbances

An efficient, model-predictive-control (MPC)-based scheme is presented for a class of consensus-related control problems involving dynamically decoupled subsystems which are required to reach a consensus condition in some optimal way. A general case of constrained subsystems with external disturbances is considered and a suitable set-based near-consensus condition is set as the target condition to achieve. The proposed scheme employs computationally efficient, closed-loop MPC policies in the subsystems together with a distributed optimization method to optimize the global consensus trajectory and the subsystem control inputs in real time. It also allows the incorporation of computational delays in the policy formulation so that the desired control performance is ensured.