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
Conference or Workshop Item, Bibliographie
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.
Item Type: | Conference or Workshop Item |
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Erschienen: | 2023 |
Creators: | Pohlodek, Johannes ; Alsmeier, Hendrik ; Morabito, Bruno ; Schlauch, Christian ; Savchenko, Anton ; Findeisen, Rolf |
Type of entry: | Bibliographie |
Title: | Stochastic Model Predictive Control Utilizing Bayesian Neural Networks |
Language: | English |
Date: | 3 July 2023 |
Publisher: | IEEE |
Book Title: | 2023 American Control Conference (ACC 2023) |
Event Title: | 2023 American Control Conference |
Event Location: | San Diego, USA |
Event Dates: | 31.05.2023-02.06.2023 |
DOI: | 10.23919/ACC55779.2023.10156115 |
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. |
Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
Date Deposited: | 17 Jul 2023 10:18 |
Last Modified: | 17 Jul 2023 10:18 |
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