21 citations · 34 across the 47 of their papers we have counts for
49 papers
SPPID: Saddle-Point PID for Constrained Optimization
Veronica Centorrino, Rawan Hoteit, Efe C. Balta +1
This paper studies constrained optimization problems through the lens of feedback control. Building on the interpretation of Lagrange multipliers as feedback controllers, we propos…
Model-Agnostic Meta Learning for Differentiable MPC
Salma Elfeki, Riccardo Zuliani, Niklas Schmid +2
Applying policy optimization to Model Predictive Control (MPC) yields high-performance and reliable controllers. However, the resulting controllers often overfit their training con…
Multi-scale closed-loop melt pool control for LPBF via policy optimization
Junan Lin, Riccardo Zuliani, Baris Kavas +3
Laser powder bed fusion (LPBF) is a metal additive manufacturing process where temperature stabilization is of vital importance to avoid defects such as distortion and cracking. Ex…
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…
A Unified Control-Theoretic Framework for Saddle-Point Dynamics in Constrained Optimization
Veronica Centorrino, Rawan Hoteit, Efe C. Balta +1
This paper studies equality-constrained minimization problems through the lens of feedback control. We introduce a unified control-theoretic framework by showing that a PID feedbac…
Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model-based policy optimization is a well-established framework for designing reliable and high-performance controllers across a wide range of control applications. Recently, this…
One-Shot Camera-Based Extrusion Optimization for High Speed Fused Filament Fabrication
Yufan Lin, Xavier Guidetti, Yannick Nagel +2
Off-the-shelf fused filament fabrication 3D printers are widely accessible and convenient, yet they exhibit quality loss at high speeds due to dynamic mis-synchronization between p…
Layer-to-layer Closed-loop Switched Heating and Cooling Control of the Laser Powder Bed Fusion Process
Barış Kavas, Efe C. Balta, Lars Witte +3
This study investigates the stabilization of interlayer temperature in the laser powder bed fusion process through a novel switched layer-to-layer closed-loop feedback controller.…
Closed Loop Reference Optimization for Extrusion Additive Manufacturing
Rawan Hoteit, Andrea Balestra, Nathan Mingard +2
Various defects occur during material extrusion additive manufacturing processes that degrade the quality of the 3D printed parts and lead to significant material waste. This motiv…
Policy Optimization for Unknown Systems using Differentiable Model Predictive Control
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model-based policy optimization often struggles with inaccurate system dynamics models, leading to suboptimal closed-loop performance. This challenge is especially evident in Model…
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…
Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers
Panagiotis D. Grontas, Antonio Terpin, Efe C. Balta +2
We introduce an output layer for neural networks that ensures satisfaction of convex constraints. Our approach, net, leverages operator splitting for rapid and reliable projecti…
Inside madupite: Technical Design and Performance
Matilde Gargiani, Robin Sieber, Philip Pawlowsky +1
In this work, we introduce and benchmark madupite, a newly proposed high-performance solver designed for large-scale discounted infinite-horizon Markov decision processes with fini…
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
Francesco Micheli, Efe C. Balta, Anastasios Tsiamis +1
We address the challenge of sequential data-driven decision-making under context distributional uncertainty. This problem arises in numerous real-world scenarios where the learner…
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…
madupite: A High-Performance Distributed Solver for Large-Scale Markov Decision Processes
Matilde Gargiani, Robin Sieber, Philip Pawlowsky +2
This paper introduces madupite, a high-performance distributed solver for large-scale Markov Decision Processes (MDPs). MDPs are widely used to model complex dynamical systems in v…
Secure Data Reconstruction: A Direct Data-Driven Approach
Jiaqi Yan, Ivan Markovsky, John Lygeros
This paper addresses the problem of secure data reconstruction for unknown systems, where data collected from the system are susceptible to malicious manipulation. We aim to recove…
Semismooth Newton Methods for Risk-Averse Markov Decision Processes
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis +1
Inspired by semismooth Newton methods, we propose a general framework for designing solution methods with convergence guarantees for risk-averse Markov decision processes. Our appr…
Operator Splitting for Convex Constrained Markov Decision Processes
Panagiotis D. Grontas, Anastasios Tsiamis, John Lygeros
We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers,…
Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems
Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger +2
We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular…
Iterative Learning Control for Ramp Metering on Service Station On-ramps
Hongxi Xiang, Carlo Cenedese, Efe C. Balta +1
Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow…
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…
Online Residual Learning from Offline Experts for Pedestrian Tracking
Anastasios Vlachos, Anastasios Tsiamis, Aren Karapetyan +2
In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-tra…
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…