NDLI: Extended recursively feasible Model Predictive Control by two-stage online optimization

Content Provider	IEEE Xplore Digital Library
Author	Pin, G. Parisini, T.
Copyright Year	2010
Description	Author affiliation: Dept. of Electr., Electron. & Comput. Eng., Univ. of Trieste, Trieste, Italy (Parisini, T.) \|\| Danieli Autom. S.p.A. (UD), Italy (Pin, G.)
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.
Starting Page	5483
Ending Page	5488
File Size	1011513
Page Count	6
File Format	PDF
ISBN	9781424474264
ISSN	07431619
e-ISBN	9781424474271
DOI	10.1109/ACC.2010.5530974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Predictive models Predictive control Uncertainty Robust control Constraint optimization Robustness Control systems Set theory Nonlinear systems Constraint theory
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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