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Main Author: Karacelik, Asli
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2510.05338
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author Karacelik, Asli
author_facet Karacelik, Asli
contents In this review, we assess the use of Bayesian methods in model predictive control (MPC), focusing on neural-network-based modeling, control design, and uncertainty quantification. We systematically analyze individual studies and how they are implemented in practice. While Bayesian approaches are increasingly adopted to capture and propagate uncertainty in MPC, reported gains in performance and robustness remain fragmented, with inconsistent baselines and limited reliability analyses. We therefore argue for standardized benchmarks, ablation studies, and transparent reporting to rigorously determine the effectiveness of Bayesian techniques for MPC.
format Preprint
id arxiv_https___arxiv_org_abs_2510_05338
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Integrating Bayesian methods with neural network--based model predictive control: a review
Karacelik, Asli
Artificial Intelligence
Machine Learning
In this review, we assess the use of Bayesian methods in model predictive control (MPC), focusing on neural-network-based modeling, control design, and uncertainty quantification. We systematically analyze individual studies and how they are implemented in practice. While Bayesian approaches are increasingly adopted to capture and propagate uncertainty in MPC, reported gains in performance and robustness remain fragmented, with inconsistent baselines and limited reliability analyses. We therefore argue for standardized benchmarks, ablation studies, and transparent reporting to rigorously determine the effectiveness of Bayesian techniques for MPC.
title Integrating Bayesian methods with neural network--based model predictive control: a review
topic Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2510.05338