Abstract

This paper presents an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with both matched and unmatched nonlinear uncertainties subject to both state and input constraints. In particular, the proposed control framework leverages an L1 adaptive controller (L1 Ac) to compensate for the matched uncertainties and to provide guaranteed uniform bounds on the error between the states and control inputs of the actual system and those of a nominal i.e., uncertainty-free, system. The performance bounds provided by the L1 AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints. The proposed control framework, which we denote as uncertainty compensation-based MPC (UC-MPC), guarantees constraint satisfaction and achieves improved performance compared with existing methods. Simulation results on a flight control example demonstrate the benefits of the proposed framework.

Original languageEnglish (US)
Title of host publication2024 American Control Conference, ACC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1873-1878
Number of pages6
ISBN (Electronic)9798350382655
DOIs
StatePublished - 2024
Event2024 American Control Conference, ACC 2024 - Toronto, Canada
Duration: Jul 10 2024Jul 12 2024

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Conference

Conference2024 American Control Conference, ACC 2024
Country/TerritoryCanada
CityToronto
Period7/10/247/12/24

Keywords

  • Adaptive control
  • Constrained control
  • Model predictive control
  • Robust control
  • Uncertainty compensation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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