Sopasakis, Pantelis and Bernardini, Daniele and Bemporad, Alberto Constrained Model Predictive Control Based on Reduced-Order Models. In: 52nd IEEE Conference on Decision and Control. IEEE, pp. 7071-7076. ISBN 978-1-4673-5717-3 (2013)Full text not available from this repository.
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.
|Item Type:||Book Section|
|Additional Information:||52nd IEEE Conference on Decision and Control held in Florence, Italy, December 10-13, 2013|
|Uncontrolled Keywords:||Predictive control for linear systems, Model/Controller reduction, Large-scale systems|
|Subjects:||Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Research Area:||Computer Science and Applications|
|Depositing User:||Pantelis Sopasakis|
|Date Deposited:||25 Oct 2013 08:56|
|Last Modified:||12 Jun 2014 10:09|
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