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Title: Online learning‐based model predictive control with Gaussian process models and stability guarantees
Author(s): Maiworm, MichaelLook up in the Integrated Authority File of the German National Library
Limon, Daniel
Findeisen, RolfLook up in the Integrated Authority File of the German National Library
Issue Date: 2021
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-878756
Subjects: Gaussian processes
Input-to-state stability
Machine learning
Online learning
Predictive control
Recursive updates
Abstract: Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlyingmodel, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process-based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input-to-state stability with respect to the model–plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance.
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: Projekt DEAL 2020
Journal Title: International journal of robust and nonlinear control
Publisher: Wiley
Publisher Place: New York, NY [u.a.]
Volume: 31
Issue: 18
Original Publication: 10.1002/rnc.5361
Page Start: 8785
Page End: 8812
Appears in Collections:Fakultät für Elektrotechnik und Informationstechnik (OA)

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