Guided multi-fidelity bayesian optimization for data-driven controller tuning with digital twins
Loading...
Author (Corporation)
Publication date
2026
Type of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
IEEE Robotics and Automation Letters
Special issue
DOI of the original publication
Link
Related research data
Series
Series number
Volume
11
Issue / Number
5
Pages / Duration
5294-5301
Patent number
Publisher / Publishing institution
IEEE
Place of publication / Event location
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
We propose a guided multi-fidelity Bayesian optimization framework for data-efficient controller tuning that integrates corrected digital twin simulations with real-world measurements. The method targets closed-loop systems with limited-fidelity simulations or inexpensive approximations. To address model mismatch, we build a multi-fidelity surrogate with a learned correction model that refines digital twin estimates using real data. An adaptive cost-aware acquisition function balances expected improvement, fidelity, and sampling cost. Our method ensures adaptability as new measurements arrive. The digital twin accuracy is re-estimated, dynamically adapting both cross-source correlations and the acquisition function. This ensures that accurate simulations are used more frequently, while inaccurate simulation data are appropriately downweighted. Experiments on robotic drive hardware and supporting numerical studies demonstrate that our method enhances tuning efficiency compared to standard Bayesian optimization and multi-fidelity methods.
Keywords
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
2377-3774
2377-3766
2377-3766
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
Citation
Nobar, M., Keller, J. P., Forino, A., Lygeros, J., & Rupenyan, A. (2026). Guided multi-fidelity bayesian optimization for data-driven controller tuning with digital twins. IEEE Robotics and Automation Letters, 11(5), 5294–5301. https://doi.org/10.1109/lra.2026.3671557