Stochastic Model Predictive Control Utilizing Bayesian Neural Networks
This work addresses control system design for domains like wastewater treatment, offering an incremental improvement by substituting Bayesian neural networks for Gaussian processes in existing frameworks.
The authors tackled the challenge of ensuring performance and safety in learning-based control systems by exploring Bayesian neural networks as an alternative to Gaussian processes for stochastic model predictive control, applied to a wastewater treatment plant model. Results showed Bayesian neural networks achieve similar performance, highlighting their potential for handling extensive data sets.
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.