Conference: 52nd IEEE Annual Conference on Decision and Control (CDC), Florence, Italy. 10-13 December, 2013
Authors: P. Sopasakis, D. Bernardini, and A. Bemporad
Abstract: 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.
Oral presentation (13 December) available at: Constrained Model Predictive Control Based on Reduced-Order Models. Presentation slides. 13 December, 2013