21 citations · 34 across the 47 of their papers we have counts for
23 papers · 1 filter
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…
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter +2
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from obs…
Adaptive Learning-based Model Predictive Control for Uncertain Interconnected Systems: A Set Membership Identification Approach
Ahmed Aboudonia, John Lygeros
We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsyst…
Swarm-Based Trajectory Generation and Optimization for Stress-Aligned 3D Printing
Xavier Guidetti, Efe C. Balta, John Lygeros
In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to s…
Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes
Matilde Gargiani, Robin Sieber, Efe Balta +2
We consider inexact policy iteration methods for large-scale infinite-horizon discounted MDPs with finite spaces, a variant of policy iteration where the policy evaluation step is…
A Coupled Optimization Framework for Correlated Equilibria in Normal-Form Game
Sarah H. Q. Li, Yue Yu, Florian Dörfler +1
In competitive multi-player interactions, simultaneous optimality is a key requirement for establishing strategic equilibria. This property is explicit when the game-theoretic equi…
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…
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Aren Karapetyan, Efe C. Balta, Anastasios Tsiamis +2
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the…
Finite Sample Frequency Domain Identification
Anastasios Tsiamis, Mohamed Abdalmoaty, Roy S. Smith +1
We study non-parametric frequency-domain system identification from a finite-sample perspective. We assume an open loop scenario where the excitation input is periodic and consider…
Data-Driven Extrusion Force Control Tuning for 3D Printing
Xavier Guidetti, Ankita Mukne, Marvin Rueppel +3
The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods i…
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…
Control Strategies for Recommendation Systems in Social Networks
Ben Sprenger, Giulia De Pasquale, Raffaele Soloperto +2
A closed-loop control model to analyze the impact of recommendation systems on opinion dynamics within social networks is introduced. The core contribution is the development and f…
Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
Mohamed Abdalmoaty, Efe C. Balta, John Lygeros +1
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other han…
Adaptive Economic Model Predictive Control for linear systems with performance guarantees
Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger +2
We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters…
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…
Data-Enabled Predictive Iterative Control
Kai Zhang, Riccardo Zuliani, Efe C. Balta +1
This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems. The DeePRC learns from previous iter…
Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We…
Predictive Linear Online Tracking for Unknown Targets
Anastasios Tsiamis, Aren Karapetyan, Yueshan Li +2
In this paper, we study the problem of online tracking in linear control systems, where the objective is to follow a moving target. Unlike classical tracking control, the target is…