Paper Info
Title
Extended Recursively Feasible Model Predictive Control By Two-Stage Online Optimization
Abstract
In this work, a novel Model Predictive Control (MPC) scheme for the robust state-feedback stabilization of constrained discrete-time linear and nonlinear systems is proposed. In the last few years, invariant set theory has provided sufficient conditions to ensure the recursive feasibility of the constrained optimization problem associated to the MPC. In particular, it has emerged that the robustness of the classical MPC with stabilizing terminal state constraint depends on the invariance properties of the specified final constraint set. In this framework, with the aim to enlarge the set of admissible perturbations beyond the limit of one-step recursive feasibility, an algorithm based on two-stage optimization is presented. When only practical stabilization is needed, the devised method allows to use as terminal constraint also sets which are not one-step robustly controllable, while preserving the extended recursive feasibility property.
Year
DOI
Venue
2010
10.1109/ACC.2010.5530974
2010 AMERICAN CONTROL CONFERENCE
Keywords
DocType
ISSN
nonlinear system,linear systems,discrete time,predictive control,additives,economic indicators,robustness,optimization,model predictive control,trajectory,hafnium,robust control,uncertainty,constrained optimization,invariance
Conference
0743-1619
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Gilberto Pin113617.21
T Parisini2935113.17