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Dynamic uncertainties in model predictive control: Guaranteed stability for constrained linear systems

Lukas Schwenkel, Johannes Kohler, Matthias Müller, Frank Allgower

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Abstract

In this work, we propose a tube-based model predictive control (MPC) scheme for state and input constrained linear systems that are subject to dynamic uncertainties de-scribed by integral quadratic constraints (IQCs). We extend the framework of verifying exponential decay rates with IQCs in order to derive an exponentially stable scalar system that bounds the error between the nominal prediction model and the actual unknown system. In the proposed MPC scheme, this error bounding system is predicted together with the nominal model to define the size of the tube. We prove that this scheme achieves robust constraint satisfaction and input-to-state stability, and we demonstrate the flexibility of dynamic tubes in a numerical example.

Original languageEnglish
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
Pages1235-1241
Number of pages7
ISBN (Electronic)9781728174471
DOIs
Publication statusPublished - 2020
Event2020 59th IEEE Conference on Decision and Control (CDC) - Jeju, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference2020 59th IEEE Conference on Decision and Control (CDC)
Country/TerritoryKorea, Republic of
CityJeju
Period14 Dec 202018 Dec 2020

ASJC Scopus subject areas

  • Control and Optimization
  • Control and Systems Engineering
  • Modelling and Simulation

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