no code implementations • 26 Sep 2024 • Jialin Li, Marta Zagorowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros

Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.

no code implementations • 6 Sep 2024 • Anastasios Vlachos, Anastasios Tsiamis, Aren Karapetyan, Efe C. Balta, John Lygeros

In this paper, we consider the problem of predicting unknown targets from data.

no code implementations • 27 Jun 2024 • Baris Kavas, Efe C. Balta, Michael R. Tucker, Raamadaas Krishnadas, Alisa Rupenyan, John Lygeros, Markus Bambach

In summary, BO presents a promising method for automatic in-layer controller tuning in LPBF, enhancing control precision and mitigating overheating in production parts.

1 code implementation • 24 May 2024 • Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter, Volkan Cevher, John Lygeros

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 observed optimal behavior.

no code implementations • 25 Apr 2024 • Ahmed Aboudonia, John Lygeros

Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data.

no code implementations • 18 Apr 2024 • Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan, John Lygeros

The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients.

no code implementations • 2 Apr 2024 • Aren Karapetyan, Efe C. Balta, Anastasios Tsiamis, Andrea Iannelli, John Lygeros

Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty.

no code implementations • 1 Apr 2024 • Anastasios Tsiamis, Mohamed Abdalmoaty, Roy S. Smith, John Lygeros

The error rate is of the order of $\mathcal{O}((d_{\mathrm{u}}+\sqrt{d_{\mathrm{u}}d_{\mathrm{y}}})\sqrt{M/N_{\mathrm{tot}}})$, where $N_{\mathrm{tot}}$ is the total number of samples, $M$ is the number of desired frequencies, and $d_{\mathrm{u}},\, d_{\mathrm{y}}$ are the dimensions of the input and output signals respectively.

no code implementations • 27 Mar 2024 • Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger, John Lygeros, Johannes Köhler

We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters.

no code implementations • 25 Mar 2024 • Mahdi Nobar, Jürg Keller, Alisa Rupenyan, Mohammad Khosravi, John Lygeros

This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on available closed-loop data.

no code implementations • 24 Mar 2024 • Xavier Guidetti, Nathan Mingard, Raul Cruz-Oliver, Yannick Nagel, Marvin Rueppel, Alisa Rupenyan, Efe C. Balta, John Lygeros

In material extrusion additive manufacturing, the extrusion process is commonly controlled in a feed-forward fashion.

no code implementations • 18 Mar 2024 • Kai Zhang, Riccardo Zuliani, Efe C. Balta, John Lygeros

This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems.

no code implementations • 10 Mar 2024 • Ben Sprenger, Giulia De Pasquale, Raffaele Soloperto, John Lygeros, Florian Dörfler

A closed-loop control model to analyze the impact of recommendation systems on opinion dynamics within social networks is introduced.

no code implementations • 9 Mar 2024 • Mohamed Abdalmoaty, Efe C. Balta, John Lygeros, Roy S. Smith

It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators.

no code implementations • 7 Mar 2024 • 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.

no code implementations • 15 Feb 2024 • Anastasios Tsiamis, Aren Karapetyan, Yueshan Li, Efe C. Balta, John Lygeros

The learned model is used in the optimal policy under the framework of receding horizon control.

no code implementations • 25 Jan 2024 • Florian Dörfler, Zhiyu He, Giuseppe Belgioioso, Saverio Bolognani, John Lygeros, Michael Muehlebach

Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence.

no code implementations • 9 Dec 2023 • Aren Karapetyan, Efe C. Balta, Andrea Iannelli, John Lygeros

Finite-time guarantees allow the control design to distribute a limited computational budget over a time horizon and estimate the on-the-go loss in performance due to sub-optimality.

no code implementations • 7 Dec 2023 • Alexandros Tanzanakis, John Lygeros

By solving a parameterized optimal tracking control problem subject to the unknown nominal system and a suitable cost function, the resulting optimal tracking control policy can ensure closed-loop stability by achieving a sufficiently small tracking error for the original uncertain nonlinear system.

no code implementations • 16 Nov 2023 • Dominic Liao-McPherson, Efe C. Balta, Mohamadreza Afrasiabi, Alisa Rupenyan, Markus Bambach, John Lygeros

Additive manufacturing processes are flexible and efficient technologies for producing complex geometries.

no code implementations • 16 Nov 2023 • Alessio Rimoldi, Carlo Cenedese, Alberto Padoan, Florian Dörfler, John Lygeros

Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers.

no code implementations • 6 Nov 2023 • Alexandros Tanzanakis, John Lygeros

We consider the problem of optimal tracking control of unknown discrete-time nonlinear nonzero-sum games.

no code implementations • 30 Oct 2023 • Lucia Falconi, Andrea Martinelli, John Lygeros

The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic programming setting.

no code implementations • 27 Oct 2023 • Varsha Behrunani, Philipp Heer, John Lygeros

As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory.

no code implementations • 26 Oct 2023 • Marta Zagorowska, Christopher König, Hanlin Yu, Efe C. Balta, Alisa Rupenyan, John Lygeros

The performance of the new method is first validated in a simulated precision motion system, demonstrating improved computational efficiency, and illustrating the role of exploiting numerical solvers to reach the desired precision.

no code implementations • 10 Sep 2023 • Kenan Zhang, John Lygeros

This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market.

no code implementations • 24 Jul 2023 • Samuel Balula, Efe C. Balta, Dominic Liao-McPherson, Alisa Rupenyan, John Lygeros

We present simulations to illustrate the performance of the proposed method for linear and nonlinear dynamics models.

no code implementations • 4 Jul 2023 • Varsha Behrunani, Marta Zagorowska, Mathias Hudoba de Badyn, Francesco Ricca, Philipp Heer, John Lygeros

Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems.

no code implementations • 26 Jun 2023 • Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion.

no code implementations • 17 May 2023 • Aren Karapetyan, Efe C. Balta, Andrea Iannelli, John Lygeros

Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions.

no code implementations • 17 May 2023 • Milos Katanic, John Lygeros, Gabriela Hug

Power systems are highly complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging.

1 code implementation • 28 Apr 2023 • Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances.

no code implementations • 27 Apr 2023 • Varsha Behrunani, Hanmin Cai, Philipp Heer, Roy S. Smith, John Lygeros

Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource.

no code implementations • 24 Apr 2023 • Varsha Behrunani, Francesco Micheli, Jonas Mehr, Philipp Heer, John Lygeros

Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands.

no code implementations • 24 Apr 2023 • Panagiotis D. Grontas, Carlo Cenedese, Marta Fochesato, Giuseppe Belgioioso, John Lygeros, Florian Dörfler

We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level.

no code implementations • 6 Apr 2023 • Marta Fochesato, Filippo Fabiani, John Lygeros

We consider generalized Nash equilibrium problems (GNEPs) with linear coupling constraints affected by both local (i. e., agent-wise) and global (i. e., shared resources) disturbances taking values in polyhedral uncertainty sets.

no code implementations • 17 Mar 2023 • Aren Karapetyan, Diego Bolliger, Anastasios Tsiamis, Efe C. Balta, John Lygeros

Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions.

no code implementations • 9 Feb 2023 • Varsha Behrunani, Andrew Irvine, Giuseppe Belgioioso, Philipp Heer, John Lygeros, Florian Dörfler

Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory.

no code implementations • 18 Nov 2022 • Carlo Cenedese, Michele Cucuzzella, Adriano Cotta Ramusino, Davide Spalenza, John Lygeros, Antonella Ferrara

We focus on the problem of optimally designing a service station to achieve beneficial effects in terms of total traffic congestion and peak traffic reduction.

no code implementations • 15 Nov 2022 • Samuel Balula, Dominic Liao-McPherson, Stefan Stevšić, Alisa Rupenyan, John Lygeros

Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses.

no code implementations • 14 Nov 2022 • Aren Karapetyan, Anastasios Tsiamis, Efe C. Balta, Andrea Iannelli, John Lygeros

The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning.

1 code implementation • 14 Nov 2022 • Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

We consider control of dynamical systems through the lens of competitive analysis.

no code implementations • 14 Nov 2022 • Niklas Schmid, John Lygeros

We consider the safety evaluation of discrete time, stochastic systems over a finite horizon.

no code implementations • 14 Nov 2022 • Giuseppe Belgioioso, Dominic Liao-McPherson, Mathias Hudoba de Badyn, Nicolas Pelzmann, John Lygeros, Florian Dörfler

In distributed model predictive control (MPC), the control input at each sampling time is computed by solving a large-scale optimal control problem (OCP) over a finite horizon using distributed algorithms.

no code implementations • 27 Oct 2022 • Xavier Guidetti, Marino Kühne, Yannick Nagel, Efe C. Balta, Alisa Rupenyan, John Lygeros

The tuning of fused filament fabrication parameters is notoriously challenging.

no code implementations • 15 Aug 2022 • Ezzat Elokda, Carlo Cenedese, Kenan Zhang, Andrea Censi, John Lygeros, Emilio Frazzoli, Florian Dörfler

In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion.

no code implementations • 25 Jul 2022 • Milos Katanic, John Lygeros, Gabriela Hug

Power network and generators state estimation are usually tackled as separate problems.

no code implementations • 19 Jul 2022 • Ahmed Aboudonia, Goran Banjac, Annika Eichler, John Lygeros

A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline.

no code implementations • 19 Jul 2022 • Ahmed Aboudonia, Andrea Martinelli, Nicolas Hoischen, John Lygeros

In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved.

no code implementations • 4 Jun 2022 • Linbin Huang, John Lygeros, Florian Dörfler

This paper presents a robust and kernelized data-enabled predictive control (RoKDeePC) algorithm to perform model-free optimal control for nonlinear systems using only input and output data.

no code implementations • 31 May 2022 • Samuel Balula, Dominic Liao-McPherson, Alisa Rupenyan, John Lygeros

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 without modifying any built-in low-level controllers.

no code implementations • 28 May 2022 • Efe C. Balta, Mohammad H. Mamduhi, John Lygeros, Alisa Rupenyan

In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks.

no code implementations • 24 May 2022 • Xavier Guidetti, Alisa Rupenyan, Lutz Fassl, Majid Nabavi, John Lygeros

We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization.

no code implementations • 23 May 2022 • Carlo Cenedese, Michele Cucuzzella, Antonella Ferrara, John Lygeros

In this paper, we propose a novel model that describes how the traffic evolution on a highway stretch is affected by the presence of a service station.

no code implementations • 19 Apr 2022 • Marta Fochesato, Carlo Cenedese, John Lygeros

In modern buildings renewable energy generators and storage devices are spreading, and consequently the role of the users in the power grid is shifting from passive to active.

no code implementations • 10 Apr 2022 • Efe C. Balta, Andrea Iannelli, Roy S. Smith, John Lygeros

In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the system toward a desired reference trajectory.

no code implementations • 31 Mar 2022 • Benjamin Gravell, Matilde Gargiani, John Lygeros, Tyler H. Summers

We propose a policy iteration algorithm for solving the multiplicative noise linear quadratic output feedback design problem.

no code implementations • 22 Mar 2022 • Andrea Martinelli, Matilde Gargiani, Marina Draskovic, John Lygeros

In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming.

no code implementations • 10 Mar 2022 • Dominic Liao-McPherson, Efe C. Balta, Alisa Rupenyan, John Lygeros

Iterative learning control (ILC) is a control strategy for repetitive tasks wherein information from previous runs is leveraged to improve future performance.

1 code implementation • 1 Mar 2022 • Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods.

no code implementations • 1 Feb 2022 • Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler Summers, John Lygeros

A numerical evaluation confirms the competitive performance of our method on classical control tasks.

no code implementations • 31 Dec 2021 • Angeliki Kamoutsi, Goran Banjac, John Lygeros

We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations.

no code implementations • 28 Dec 2021 • Angeliki Kamoutsi, Goran Banjac, John Lygeros

We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations.

no code implementations • 19 Nov 2021 • Efe C. Balta, Kira Barton, Dawn M. Tilbury, Alisa Rupenyan, John Lygeros

In this work, we develop an iterative approach for repetitive precision motion control problems where the objective is to follow a reference geometry with minimal tracking error.

no code implementations • 16 Nov 2021 • Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan, John Lygeros

Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products.

no code implementations • 1 Nov 2021 • Dominic Liao-McPherson, Efe C. Balta, Ryan Wüest, Alisa Rupenyan, John Lygeros

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 desired geometry.

no code implementations • 29 Oct 2021 • Felix Bünning, Benjamin Huber, Adrian Schalbetter, Ahmed Aboudonia, Mathias Hudoba de Badyn, Philipp Heer, Roy S. Smith, John Lygeros

However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models.

no code implementations • 13 Sep 2021 • Anilkumar Parsi, Ahmed Aboudonia, Andrea Iannelli, John Lygeros, Roy S. Smith

Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation.

no code implementations • 15 Jul 2021 • Ahmed Aboudonia, Andrea Martinelli, John Lygeros

The controller is synthesized by locally solving a semidefinite program offline for each subsystem in a decentralized fashion.

no code implementations • 15 May 2021 • Linbin Huang, Jianzhe Zhen, John Lygeros, Florian Dörfler

The proposed framework enables us to obtain model-free optimal control for LTI systems based on noisy input/output data.

no code implementations • 29 Mar 2021 • Alisa Rupenyan, Mohammad Khosravi, John Lygeros

Accurate positioning and fast traversal times determine the productivity in machining applications.

no code implementations • 25 Mar 2021 • Xavier Guidetti, Alisa Rupenyan, Lutz Fassl, Majid Nabavi, John Lygeros

Recent work has shown constrained Bayesian optimization to be a powerful technique for the optimization of industrial processes.

no code implementations • 19 Jan 2021 • Christopher König, Matteo Turchetta, John Lygeros, Alisa Rupenyan, Andreas Krause

Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization.

no code implementations • 8 Dec 2020 • Linbin Huang, Jianzhe Zhen, John Lygeros, Florian Dörfler

Moreover, when the input/output constraints are inactive, the quadratic regularization leads to a closed-form solution of the DeePC algorithm and thus enables fast calculations.

no code implementations • 26 Nov 2020 • Felix Bünning, Adrian Schalbetter, Ahmed Aboudonia, Mathias Hudoba de Badyn, Philipp Heer, John Lygeros

We assess the consequences of the additional constraints for the model accuracy and test the models in a real-life MPC experiment in an apartment in Switzerland.

no code implementations • 26 Nov 2020 • Eugenio Chisari, Alexander Liniger, Alisa Rupenyan, Luc van Gool, John Lygeros

We present a reinforcement learning-based solution to autonomously race on a miniature race car platform.

no code implementations • 28 Oct 2020 • Christopher König, Mohammad Khosravi, Markus Maier, Roy S. Smith, Alisa Rupenyan, John Lygeros

This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.

no code implementations • 15 Sep 2020 • Felix Bünning, Joseph Warrington, Philipp Heer, Roy S. Smith, John Lygeros

By combining a control scheme based on Robust Model Predictive Control, with affine policies, and heating demand forecasting based on Artificial Neural Networks with online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and buffer storage.

no code implementations • 1 Jul 2020 • Ahmed Aboudonia, Annika Eichler, Francesco Cordiano, Goran Banjac, John Lygeros

The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.

no code implementations • L4DC 2020 • Sandeep Menta, Joseph Warrington, John Lygeros, Manfred Morari

Hybrid control problems are complicated by the need to make a suitable sequence of discrete decisions related to future modes of operation of the system.

1 code implementation • 9 Dec 2019 • Michel Schubiger, Goran Banjac, John Lygeros

The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems.

Optimization and Control

no code implementations • 27 Nov 2019 • Linbin Huang, Jeremy Coulson, John Lygeros, Florian Dörfler

Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification.

4 code implementations • 13 May 2019 • Juraj Kabzan, Miguel de la Iglesia Valls, Victor Reijgwart, Hubertus Franciscus Cornelis Hendrikx, Claas Ehmke, Manish Prajapat, Andreas Bühler, Nikhil Gosala, Mehak Gupta, Ramya Sivanesan, Ankit Dhall, Eugenio Chisari, Napat Karnchanachari, Sonja Brits, Manuel Dangel, Inkyu Sa, Renaud Dubé, Abel Gawel, Mark Pfeiffer, Alexander Liniger, John Lygeros, Roland Siegwart

This paper presents the algorithms and system architecture of an autonomous racecar.

Robotics

no code implementations • 22 Nov 2018 • Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros, María Rodríguez Martínez, Maria Anna Rapsomaniki

In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D).

no code implementations • 24 Aug 2017 • Tobias Sutter, David Sutter, Peyman Mohajerin Esfahani, John Lygeros

We consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise.

no code implementations • 8 Mar 2017 • Alain Kibangou, Alexander Artikis, Evangelos Michelioudakis, Georgios Paliouras, Marius Schmitt, John Lygeros, Chris Baber, Natan Morar, Fabiana Fournier, Inna Skarbovsky

Traffic on freeways can be managed by means of ramp meters from Road Traffic Control rooms.

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