TY  - CHAP
T2  - 52nd IEEE Conference on Decision and Control
KW  - Predictive control for linear systems
KW  -  Model/Controller reduction
KW  -  Large-scale systems
Y1  - 2013/12//
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6761010&isnumber=6759837
A1  - Sopasakis, Pantelis
A1  - Bernardini, Daniele
A1  - Bemporad, Alberto
PB  - IEEE
EP  - 7076
ID  - eprints1844
N2  - The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control. In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.
SP  - 7071
TI  - Constrained Model Predictive Control Based on Reduced-Order Models
N1  - 52nd IEEE Conference on Decision and Control held in Florence, Italy, December 10-13, 2013
AV  - none
SN  - 978-1-4673-5717-3
ER  -