Publication details


M.N. Zeilinger, Y. Pu, S. Riverso, G. Ferrari-Trecate, and C.N. Jones. Plug and play distributed model predictive control based on distributed invariance and optimization. Proc. 52nd IEEE Conference on Decision and Control , pages 5770--5776, 2013. Florence, Italy, December 10-13.


This paper presents a method for plug-and-play distributed MPC of a network of interacting linear systems. The previously introduced idea of plug and play control addresses the challenge of performing network changes in the form of subsystems that are joining or leaving the network during closed-loop operation, while maintaining stability and con- straint satisfaction. This work extends these ideas to an iterative distributed MPC scheme for systems with strong coupling by employing a recently proposed method for distributed MPC that takes the coupling dynamics into account in the form of time-varying terminal sets and distributed optimization. A distributed synthesis procedure for the update of the local control laws is proposed together with a transition scheme preparing the system for the upcoming modifications. This enables automatic plug-and-play operation, including rejection if the new network topology is infeasible. Both the synthesis and online control are entirely distributed and are only based on local information on the subsystems and their coupled neighbors. Finally, the proposed scheme is applied to the problem of frequency control in a power network.