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Authors: Ricus Husmann ; Sven Weishaupt and Harald Aschemann

Affiliation: Chair of Mechatronics, University of Rostock, Rostock, Germany

Keyword(s): Machine Learning in Control Applications.

Abstract: In this paper, we present an extension to the recursive Gaussian Process (RGP) regression that enables the satisfaction of inequality constraints and is well suited for a real-time execution in control applications. The soft inequality constraints are integrated by introducing an additional extended Kalman Filter (EKF) update step using pseudo-measurements. The sequential formulation of the algorithm and several developed heuristics ensure both the performance and a low computational effort of the algorithm. A special focus lies on an efficient consideration of monotonicity assumptions for GPs in the form of inequality constraints. The algorithm is statistically validated in simulations, where the possible advantages in comparison with the standard RGP algorithm become obvious. The paper is concluded with a successful experimental validation of the developed algorithm for the monotonicity-preserving learning of heat transfer values for the control of a vapor compression cycle evapora tor, leveraging a previously published partial input output linearization (IOL). (More)

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Paper citation in several formats:
Husmann, R., Weishaupt, S. and Aschemann, H. (2025). Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2; ISSN 2184-2809, SciTePress, pages 340-349. DOI: 10.5220/0013783100003982

@conference{icinco25,
author={Ricus Husmann and Sven Weishaupt and Harald Aschemann},
title={Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={340-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013783100003982},
isbn={978-989-758-770-2},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications
SN - 978-989-758-770-2
IS - 2184-2809
AU - Husmann, R.
AU - Weishaupt, S.
AU - Aschemann, H.
PY - 2025
SP - 340
EP - 349
DO - 10.5220/0013783100003982
PB - SciTePress

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