IRI - Multi-layer decentralized MPC of large-scale networked systems

Publication

Multi-layer decentralized MPC of large-scale networked systems

Book Chapter (2014)

Book Title

Distributed Model Predictive Control Made Easy

Publisher

Springer

Pages

495-515

Volume

Number

Serie

Intelligent Systems, Control and Automation: Science and Engineering

Doc link

http://link.springer.com/chapter/10.1007/978-94-007-7006-5_31

File

Download the digital copy of the doc pdf document

Authors

Projects associated

WATMAN: Analysis and Design of distributed optimal control strategies applied to large-scale WATer systems MANagement

Abstract

In this chapter, a multi-layer decentralized model predictive control (ML- DMPC) approach is proposed and designed for its application to large-scale net- worked systems (LSNS). This approach is based on the periodic nature of the sys- tem disturbance and the availability of both static and dynamic models of the LSNS. Hence, the topology of the controller is structured in two layers. First, an upper layer is in charge of achieving the global objectives from a set O of control objectives given for the LSNS. This layer works with a sampling time ∆t1, corresponding to the disturbances period. Second, a lower layer, with a sampling time ∆t2, ∆t1 > ∆t2, is in charge of computing the references for the system actuators in order to satisfy the local objectives from the set of control objectives O. A system partitioning al- lows to establish a hierarchical flow of information between a set C of controllers designed based on model predictive control (MPC). Therefore, the whole proposed ML-DMPC strategy results in a centralized optimization problem for considering the global control objectives, followed of a decentralized scheme for reaching the local control objectives. The proposed approach is applied to a real case study: the water transport network of Barcelona (Spain). Results obtained with selected simu- lation scenarios show the effectiveness of the proposed ML-DMPC strategy in terms of system modularity, reduced computational burden and, at the same time, the ad- missible loss of performance with respect to a centralized MPC (CMPC) strategy.

Author keywords

decentralised control, model predictive control, non-iterative control topologies, industrial applications, partitioning

Scientific reference

C. Ocampo-Martínez, V. Puig, J.M. Grosso and S. Montes. Multi-layer decentralized MPC of large-scale networked systems. In Distributed Model Predictive Control Made Easy, 495-515. Springer, 2014.