Distributed model predictive control for networks with changing topologies

Abstract

Results are presented which extend the recent distributed model predictive control approach based on dissipativity to allow for process and controller networks with changing topologies. In this unified approach, both known and unknown changes in the process and controller networks may be accounted for within the same framework. The controllers reconfigure themselves for known changes in the network topology. A robust control approach is also developed to deal with unknown variations in the topology. Closed-loop stability and minimum performance of the process network is ensured by placing a dissipative trajectory constraint on each controller. This allows for the interaction effects between units to be captured in the dissipativity properties of each process, and thus, accounted for by choosing suitable dissipativity constraints for each controller.