82 citations · 229 across the 29 of their papers we have counts for
28 papers · 1 filter
Iterative Learning Predictive Control for Constrained Uncertain Systems
Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan +1
Iterative learning control (ILC) improves the performance of a repetitive system by learning from previous trials. ILC can be combined with Model Predictive Control (MPC) to mitiga…
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…
Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors
Marta Zagorowska, Lukas Ortmann, Alisa Rupenyan +2
Online Feedback Optimization (OFO) controllers steer a system to its optimal operating point by treating optimization algorithms as auxiliary dynamic systems. Implementation of OFO…
Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion
Dominic Liao-McPherson, Efe C. Balta, Mohamadreza Afrasiabi +3
Additive manufacturing processes are flexible and efficient technologies for producing complex geometries. However, ensuring reliability and repeatability is challenging due to the…
Efficient safe learning for controller tuning with experimental validation
Marta Zagorowska, Christopher König, Hanlin Yu +3
Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms…
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
Samuel Balula, Efe C. Balta, Dominic Liao-McPherson +2
Learning-based control methods for industrial processes leverage the repetitive nature of the underlying process to learn optimal inputs for the system. While many works focus on l…
Safe Risk-averse Bayesian Optimization for Controller Tuning
Christopher Koenig, Miks Ozols, Anastasia Makarova +3
Controller tuning and parameter optimization are crucial in system design to improve both the controller and underlying system performance. Bayesian optimization has been establish…
Drone-based Volume Estimation in Indoor Environments
Samuel Balula, Dominic Liao-McPherson, Stefan Stevšić +2
Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous syste…
Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements
Xavier Guidetti, Marino Kühne, Yannick Nagel +3
The tuning of fused filament fabrication parameters is notoriously challenging. We propose an autonomous data-driven method to select parameters based on in situ measurements. We u…
Controller-Aware Dynamic Network Management for Industry 4.0
Efe C. Balta, Mohammad H. Mamduhi, John Lygeros +1
In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected…
Data-driven Reference Trajectory Optimization for Precision Motion Systems
Samuel Balula, Dominic Liao-McPherson, Alisa Rupenyan +1
We propose a data-driven optimization-based pre-compensation method to improve the contour tracking performance of precision motion stages by modifying the reference trajectory and…
On Robustness in Optimization-Based Constrained Iterative Learning Control
Dominic Liao-McPherson, Efe C. Balta, Alisa Rupenyan +1
Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance. Optimization-based IL…
Learning-Based Repetitive Precision Motion Control with Mismatch Compensation
Efe C. Balta, Kira Barton, Dawn M. Tilbury +2
Learning-based control methods utilize run-time data from the underlying process to improve the controller performance under model mismatch and unmodeled disturbances. This is bene…
Batch Model Predictive Control for Selective Laser Melting
Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan +1
Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products. A major challenge in selective laser melting is e…
In-layer Thermal Control of a Multi-layer Selective Laser Melting Process
Dominic Liao-McPherson, Efe C. Balta, Ryan Wüest +2
Selective Laser Melting (SLM) is an additive manufacturing technology that builds three dimensional parts by melting layers of metal powder together with a laser that traces out a…
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Christopher König, Matteo Turchetta, John Lygeros +2
Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approa…
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization
Alisa Rupenyan, Mohammad Khosravi, John Lygeros
Accurate positioning and fast traversal times determine the productivity in machining applications. This paper demonstrates a hierarchical contour control implementation for the in…
Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization
Xavier Guidetti, Alisa Rupenyan, Lutz Fassl +2
Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes. In complex manufacturing processes, the possibility…
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
Christopher König, Mohammad Khosravi, Markus Maier +3
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is c…
Performance-Driven Cascade Controller Tuning with Bayesian Optimization
Mohammad Khosravi, Varsha Behrunani, Piotr Myszkorowski +3
We propose a performance-based autotuning method for cascade control systems, where the parameters of a linear axis drive motion controller from two control loops are tuned jointly…