Cited by 87Advanced Control Systems OptimizationAdvanced Optimization Algorithms ResearchStability and Control of Uncertain Systems

Robust learning-based MPC for nonlinear constrained systems

Open access
Manzano, José María, Limon, Daniel, Muñoz de la Peña, David, Calliess, Jan-Peter·2020·Automatica

Abstract

Sherpa/Romeo: Versión aceptada en repositorios institucionales tras 24 meses de embargo https://v2.sherpa.ac.uk/id/publication/4278

Integrity signals

Registry

Crossref · 10.1016/j.automatica.2020.108948

Cites

32 of 32 references verified0 citation errors · 0 not checkable

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  • A data-driven MPC uses a nonparametric machine learning technique to estimate a prediction model, but it further increases the computational complexity of MPC.

    “A data-driven MPC was developed in [24], where a nonparametric machine learning technique is used to estimate a prediction model. Unfortunately, learning and data-driven techniques further increase the computational complexity of MPC.”

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Authors
Manzano, J. M., Limon, D., Muñoz de la Peña, D., & Calliess, J.-P.
Affiliations
  • Universidad de Sevilla (ES)
  • University of Oxford (GB)
Corresponding author
Manzano, José María
Venue
Automatica
Publisher
Elsevier BV
Published
2020-07
Volume
117
Pages
108948
ISSN
0005-1098 (ISSN-L)
OpenAlex
W3014803699
Cited by
87
References
32
Citations by year
2026: 5 · 2025: 22 · 2024: 12 · 2023: 13 · 2022: 19 · 2021: 12 · 2020: 4
FWCI
5.93
Type
journal article
Language
en
Open access
Locations
  • Automatica (publishedVersion)
  • Deposito de Investigacion Universidad de Sevilla (University of Seville) (submittedVersion, cc-by-nc-nd)
Funders
  • Universidad de Sevilla
  • Ministerio de Economía y Competitividad
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
  • Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
  • Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
Keywords
robust model predictive control, learning control, data-driven control

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