Pohlodek, Johannes ; Alsmeier, Hendrik ; Morabito, Bruno ; Schlauch, Christian ; Savchenko, Anton ; Findeisen, Rolf (2023)
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks.
2023 American Control Conference. San Diego, USA (31.05.2023-02.06.2023)
doi: 10.23919/ACC55779.2023.10156115
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Integrating measurements and historical data can enhance control systems through learning-based techniques, but ensuring performance and safety is challenging. Robust model predictive control strategies, like stochastic model predictive control, can address this by accounting for uncertainty. Gaussian processes are often used but have limitations with larger models and data sets. We explore Bayesian neural networks for stochastic learning-assisted control, comparing their performance to Gaussian processes on a wastewater treatment plant model. Results show Bayesian neural networks achieve similar performance, highlighting their potential as an alternative for control designs, particularly when handling extensive data sets.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Pohlodek, Johannes ; Alsmeier, Hendrik ; Morabito, Bruno ; Schlauch, Christian ; Savchenko, Anton ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Stochastic Model Predictive Control Utilizing Bayesian Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 3 Juli 2023 |
| Verlag: | IEEE |
| Buchtitel: | 2023 American Control Conference (ACC 2023) |
| Veranstaltungstitel: | 2023 American Control Conference |
| Veranstaltungsort: | San Diego, USA |
| Veranstaltungsdatum: | 31.05.2023-02.06.2023 |
| DOI: | 10.23919/ACC55779.2023.10156115 |
| Kurzbeschreibung (Abstract): | Integrating measurements and historical data can enhance control systems through learning-based techniques, but ensuring performance and safety is challenging. Robust model predictive control strategies, like stochastic model predictive control, can address this by accounting for uncertainty. Gaussian processes are often used but have limitations with larger models and data sets. We explore Bayesian neural networks for stochastic learning-assisted control, comparing their performance to Gaussian processes on a wastewater treatment plant model. Results show Bayesian neural networks achieve similar performance, highlighting their potential as an alternative for control designs, particularly when handling extensive data sets. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Hinterlegungsdatum: | 17 Jul 2023 10:18 |
| Letzte Änderung: | 17 Jul 2023 10:18 |
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