Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916714626678784 |
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| author | Lepp, Adrian Tebbe, Jörn Besginow, Andreas |
| author_facet | Lepp, Adrian Tebbe, Jörn Besginow, Andreas |
| contents | Recently, a novel linear model predictive control algorithm based on a physics-informed Gaussian Process has been introduced, whose realizations strictly follow a system of underlying linear ordinary differential equations with constant coefficients. The control task is formulated as an inference problem by conditioning the Gaussian process prior on the setpoints and incorporating pointwise soft-constraints as further virtual setpoints. We apply this method to systems of nonlinear differential equations, obtaining a local approximation through the linearization around an equilibrium point. In the case of an asymptotically stable equilibrium point convergence is given through the Bayesian inference schema of the Gaussian Process. Results for this are demonstrated in a numerical example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems Lepp, Adrian Tebbe, Jörn Besginow, Andreas Optimization and Control Systems and Control Recently, a novel linear model predictive control algorithm based on a physics-informed Gaussian Process has been introduced, whose realizations strictly follow a system of underlying linear ordinary differential equations with constant coefficients. The control task is formulated as an inference problem by conditioning the Gaussian process prior on the setpoints and incorporating pointwise soft-constraints as further virtual setpoints. We apply this method to systems of nonlinear differential equations, obtaining a local approximation through the linearization around an equilibrium point. In the case of an asymptotically stable equilibrium point convergence is given through the Bayesian inference schema of the Gaussian Process. Results for this are demonstrated in a numerical example. |
| title | Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2504.21377 |