Linear ordinary differential equations constrained Gaussian Processes for solving optimal control problems

Fuente: arXiv
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Autori principali: Besginow, Andreas, Lange-Hegermann, Markus, Tebbe, Jörn
Natura: Preprint
Pubblicazione: 2025
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author Besginow, Andreas
Lange-Hegermann, Markus
Tebbe, Jörn
author_facet Besginow, Andreas
Lange-Hegermann, Markus
Tebbe, Jörn
contents This paper presents an intrinsic approach for addressing control problems with systems governed by linear ordinary differential equations (ODEs). We use computer algebra to constrain a Gaussian Process on solutions of ODEs. We obtain control functions via conditioning on datapoints. Our approach thereby connects Algebra, Functional Analysis, Machine Learning and Control theory. We discuss the optimality of the control functions generated by the posterior mean of the Gaussian Process. We present numerical examples which underline the practicability of our approach.
format Preprint
id arxiv_https___arxiv_org_abs_2504_12775
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Linear ordinary differential equations constrained Gaussian Processes for solving optimal control problems
Besginow, Andreas
Lange-Hegermann, Markus
Tebbe, Jörn
Optimization and Control
This paper presents an intrinsic approach for addressing control problems with systems governed by linear ordinary differential equations (ODEs). We use computer algebra to constrain a Gaussian Process on solutions of ODEs. We obtain control functions via conditioning on datapoints. Our approach thereby connects Algebra, Functional Analysis, Machine Learning and Control theory. We discuss the optimality of the control functions generated by the posterior mean of the Gaussian Process. We present numerical examples which underline the practicability of our approach.
title Linear ordinary differential equations constrained Gaussian Processes for solving optimal control problems
topic Optimization and Control
url https://arxiv.org/abs/2504.12775