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Stochastic Model Predictive Control Utilizing Bayesian Neural Networks

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
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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