We discuss in this paper a novel and efficient implementation of distributed Model Predictive Control (MPC) systems for large-scale systems. The method is based on Partial Enumeration (PE), an approach that allows to compute the (sub)optimal solution of the Quadratic Program associated to the MPC problem by using a solution table that stores only a few most recently optimal active sets. This method is applied to the each local MPC system with significant improvements in terms of computational efficiency, and the original PE algorithm is modified to guarantee robust stability of the overall closedloop system. We also discuss how input constraints that involve different units, e.g. on the summation of common utility consumption, can be appropriately handled. We illustrate the benefits of proposed method by means a simulated example comprising three units.
Efficient Cooperative Distributed MPC using Partial Enumeration
PANNOCCHIA, GABRIELE;
2009-01-01
Abstract
We discuss in this paper a novel and efficient implementation of distributed Model Predictive Control (MPC) systems for large-scale systems. The method is based on Partial Enumeration (PE), an approach that allows to compute the (sub)optimal solution of the Quadratic Program associated to the MPC problem by using a solution table that stores only a few most recently optimal active sets. This method is applied to the each local MPC system with significant improvements in terms of computational efficiency, and the original PE algorithm is modified to guarantee robust stability of the overall closedloop system. We also discuss how input constraints that involve different units, e.g. on the summation of common utility consumption, can be appropriately handled. We illustrate the benefits of proposed method by means a simulated example comprising three units.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
