Publications

You can find a full list of my articles on my Google Scholar profile. Selected recent publications are featured below.

Conference Papers

Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control

Published in 2026 European Control Conference (ECC), accepted, 2026

We present an auto-tuning framework for torque-based Nonlinear MPC using high-dimensional Bayesian Optimization and a digital twin, achieving significant improvements in trajectory tracking on a UR10e robot arm.

Recommended citation: Gabriele Fadini, Deepak Ingole, Tong Duy Son, Alisa Rupenyan, European Control Conference (ECC), 2026
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Time-varying safe data-driven optimization

Published in The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), 2024

We propose a method for optimization with unknown and time-varying optimization objective and constraints, based on Bayesian optimization.

Recommended citation: Jialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros, NeurIPS, 2024
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Books

Robotics and Manufacturing Application

Published in The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024, 2024

A book chapter, focused on advanced control methods, suitable for industrial applications in robotics and precision motion systems.

Recommended citation: Alisa Rupenyan, Efe C. Balta (2024). "Robotics and Manufacturing Application." The Impact of Automatic Control Research on Industrial Innovation: Enabling a Sustainable Future, John Wiley & Sons, 2024.
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Journal Articles

Guided Multi-Fidelity Bayesian Optimization for Data-driven Controller Tuning with Digital Twins

Published in IEEE Robotics and Automation Letters, vol. 11, no. 5, 2026

We propose a guided multi-fidelity Bayesian optimization framework integrating corrected digital twin simulations with real-world measurements for data-efficient controller tuning in closed-loop robotic systems.

Recommended citation: M. Nobar, J. Keller, A. Forino, J. Lygeros, A. Rupenyan, IEEE Robotics and Automation Letters, vol. 11, no. 5, pp. 5294–5301, May 2026
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Adaptive Bayesian Optimization for High-Precision Motion Systems

Published in IEEE Transactions on Automation Science and Engineering, Volume 22, presented on IEEE IROS 2025, 2025

We propose a real-time purely data-driven, model-free approach for adaptive control by online tuning low-level controller parameters using safe Bayesian optimization.

Recommended citation: Christopher König, Raamadaas Krishnadas, Efe C. Balta, Alisa Rupenyan, IEEE Transactions on Automation Science and Engineering, Volume 22, Pages 15627–15637, 2025
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In-situ controller autotuning by Bayesian optimization for closed-loop feedback control of laser powder bed fusion process

Published in Additive Manufacturing, Elsevier, volume 99, 5 February 2025, 104641, 2025

We experimentally apply Bayesian Optimization in LPBF to tune an in-layer PI controller to modulate laser power, assessing its performance on wedge geometries prone to overheating.

Recommended citation: Barış Kavas, Efe C Balta, Michael R Tucker, Raamadaas Krishnadas, Alisa Rupenyan, John Lygeros, Markus Bambach, Additive Manufacturing, volume 99, 2025
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Efficient safe learning for controller- and process parameter tuning

Published in Engineering Applications of Artificial Intelligence, volume 143, 1 March 2025, 109894, 2025

We propose a method for safe and fast data-driven optimization by formulating a series of optimization problems instead of a grid search.

Recommended citation: M Zagorowska, C König, H Yu, EC Balta, A Rupenyan, J Lygeros Engineering Applications of Artificial Intelligence, 2025
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Safe risk-averse bayesian optimization for controller tuning

Published in IEEE Robotics and Automation Letters, presented on IEEE ICRA 2023, 2023

RAGoOSe is a novel data-driven approach that combines safe learning with risk-averse Bayesian optimization to safely tune controllers in high-precision systems with variable noise, demonstrating improved performance over traditional methods in both synthetic and real semiconductor manufacturing applications.

Recommended citation: Christopher König, Miks Ozols, Anastasia Makarova, Efe C Balta, Andreas Krause, Alisa Rupenyan (2023). "Safe risk-averse bayesian optimization for controller tuning." IEEE Robotics and Automation Letters. 8(8208 - 8215).
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