82 citations · 229 across the 29 of their papers we have counts for
40 papers
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
Yu Tang, Muhammad Zakwan, Efe Balta +2
The Flexible Job Shop Scheduling Problem (FJSP) is the optimal allocation of a set of jobs to machines. Two primary challenges persist in FJSP: the unpredictable arrival of future…
Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
Jiaqi Yan, Ankush Chakrabarty, Niklas Schmid +2
In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often ch…
Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks
Deepak Ingole, Valentin Bhend, Shiva Ganesh Murali +2
Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This pap…
Differentiable Material Point Method for the Control of Deformable Objects
Diego Bolliger, Gabriele Fadini, Markus Bambach +1
Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Materi…
Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control
Gabriele Fadini, Deepak Ingole, Tong Duy Son +1
This paper presents an auto-tuning framework for torque-based Nonlinear Model Predictive Control (nMPC), where the MPC serves as a real-time controller for optimal joint torque com…
Guided Multi-Fidelity Bayesian Optimization for Data-driven Controller Tuning with Digital Twins
Mahdi Nobar, Jürg Keller, Alessandro Forino +2
We propose a \textit{guided multi-fidelity Bayesian optimization} framework for data-efficient controller tuning that integrates corrected digital twin simulations with real-world…
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…
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…
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…
Stress Flow Guided Non-Planar Print Trajectory Optimization for Additive Manufacturing of Anisotropic Polymers
Xavier Guidetti, Efe C. Balta, Yannick Nagel +3
When manufacturing parts using material extrusion additive manufacturing and anisotropic polymers, the mechanical properties of a manufactured component are strongly dependent on t…
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…
Efficient sample selection for safe learning
Marta Zagorowska, Efe C. Balta, Varsha Behrunani +2
Ensuring safety in industrial control systems usually involves imposing constraints at the design stage of the control algorithm. Enforcing constraints is challenging if the underl…
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…