82 citations · 229 across the 29 of their papers we have counts for
6 papers · 1 filter
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Jialin Li, Marta Zagorowska, Giulia De Pasquale +2
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optima…
In-situ Controller Autotuning by Bayesian Optimization for Closed-loop Feedback Control of Laser Powder Bed Fusion Process
Baris Kavas, Efe C. Balta, Michael R. Tucker +4
Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the production of complex, high-criticality parts for various industries. This method re…
Adaptive Bayesian Optimization for High-Precision Motion Systems
Christopher König, Raamadaas Krishnadas, Efe C. Balta +1
Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient mode…
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan +1
In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a…
Force Controlled Printing for Material Extrusion Additive Manufacturing
Xavier Guidetti, Nathan Mingard, Raul Cruz-Oliver +5
In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion. The amount of material to be extruded at each printing locatio…
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
Mahdi Nobar, Jürg Keller, Alisa Rupenyan +2
This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggere…