Robust stability in predictive control with soft constraints

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper we take advantage of the primary and dual Youla parameterizations for setting up a soft constrained model predictive control (MPC) scheme for which stability is guaranteed in face of norm-bounded uncertainties. Under special conditions guarantees are also given for hard input constraints. In more detail, we parameterize the MPC predictions in terms of the primary Youla parameter and use this parameter as the online optimization variable. The uncertainty is parameterized in terms of the dual Youla parameter. Stability can then be guaranteed through small gain arguments on the loop consisting of the primary and dual Youla parameter. This is included in the MPC optimization as a constraint on the induced gain of the optimization variable. We illustrate the method with a numerical simulation example.
Original languageEnglish
Title of host publicationProceedings of the American Control Conference 2010
Publication date2010
Publication statusPublished - 2010
EventAmerican Control Conference (ACC 2010) - Baltimore, MD, United States
Duration: 3 Jun 20102 Jul 2010
http://a2c2.org/conferences/acc2010/

Conference

ConferenceAmerican Control Conference (ACC 2010)
CountryUnited States
CityBaltimore, MD
Period03/06/201002/07/2010
Internet address

Keywords

  • Model Predictive Control
  • Youla parameterization
  • Robust control

Fingerprint Dive into the research topics of 'Robust stability in predictive control with soft constraints'. Together they form a unique fingerprint.

Cite this