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John Lygeros

Prof. Dr.
John Lygeros
Director
PI
Executive Committee Member
ETH Zurich
jlygeros@ethz.ch
Curriculum Vitae
The NCCR changed my life and that of those around me.

John Lygeros grew up in Athens, Greece where he graduated from Athens College in 1987. He completed a B.Eng. degree in electrical engineering in 1990 and an M.Sc. degree in Systems Control in 1991, both at the Imperial College of Science Technology and Medicine, London, U.K.. In 1996 he obtained a Ph.D. degree from the Electrical Engineering and Computer Sciences Department, University of California, Berkeley. In the period 1996-​2000 he held a series of research appointments at the National Automated Highway Systems Consortium, M.I.T., and U.C. Berkeley. In parallel, he also worked as a part-​time research engineer at SRI International, Menlo Park, California, and as a Visiting Professor at the Department of Mathematics of the Université de Bretagne Occidentale, Brest, France. Between July 2000 and March 2003 he was a University Lecturer at the Department of Engineering, University of Cambridge, U.K., and a Fellow of Churchill College. Between March 2003 and July 2006 he was an Assistant Professor at the Department of Electrical and Computer Engineering, University of Patras, Greece. In July 2006 he joined the Automatic Control Laboratory at ETH Zurich where he is currently serving as the Professor for Computation and Control and the Head of the laboratory.

His research interests include modeling, analysis, and control of hierarchical hybrid systems, with applications to biochemical networks, large-​scale systems such as power networks, surveillance systems and air traffic management.

John Lygeros is a Fellow of the IEEE, and a member of the IET and the Technical Chamber of Greece. He is currently serving in the IEEE Board of Governors, the Scientific Steering Committee of the Newton Institute, and as the treasurer of the International Federation of Automatic Control.

Scientific Publications

Published
Online Linear Quadratic Tracking with Regret Guarantees
IEEE Control Systems Letters
Pages 6
Aren Karapetyan, Diego Bolliger, Anastasios Tsiamis, Efe Balta, John Lygeros
Doi.org
Published
Follow the Clairvoyant: An Imitation Learning Approach to Optimal Control
Proceedings of IFAC World congress 2023
Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari Trecate
Doi.org Collection Arxiv.org
Published
Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems
ArXiv
Samuel Balula, Efe Balta, Dominic Liao-McPherson, Alisa Rupenyan, John Lygeros
Collection
Published
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
ArXiv
Aren Karapetyan, Efe Balta, Andrea Iannelli, John Lygeros
Collection
Published
Stress flow guided non-planar print trajectory optimization for additive manufacturing of anisotropic polymers
Additive Manufacturing
Xavier Guidetti, Efe Balta, Yannick Nagel, Hang Yin, Alisa Rupenyan, John Lygeros
Collection
Published
On the Regret of H∞ Control
2022 IEEE Conference on Decision and Control (CDC)
Pages 6181-6186
Aren Karapetyan, Andrea Iannelli, John Lygeros
Collection
Published
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
Proceedings of the 2022 IEEE 61st Conference on Decision and Control (CDC)
Efe Balta, Andrea Iannelli, Roy Smith, John Lygeros
Collection
Published
Efficient sample selection for safe learning
ArXiv
Marta Zagorowska, Efe Balta, Varsha N. Behrunani, Alisa Rupenyan, John Lygeros
Collection
Published
Drone-based Volume Estimation in Indoor Environments
IFAC World Congress 2023
Samuel Balula, Dominic Liao-McPherson, Stefan Stevsic, Alisa Rupenyan, John Lygeros
Collection
Published
Implications of Regret on Stability of Linear Dynamical Systems
IFAC World Congress 2023
Aren Karapetyan, Anastasios Tsiamis, Efe Balta, Andrea Iannelli, John Lygeros
Doi.org Collection
Published
Information-Operation Technology Integration in Industrial Cyberphysical Systems
Computer
Vol 55 No 11 Pages 115-118
Mohammad H. Mamduhi, Efe Balta, Alisa Rupenyan, John Lygeros
Collection
Published
Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements
ArXiv
Xavier Guidetti, Marino Kühne, Yannick Nagel, Efe Balta, Alisa Rupenyan, John Lygeros
Collection
Published
Controller-Aware Dynamic Network Management for Industry 4.0
IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society
Efe Balta, Mohammad H. Mamduhi, John Lygeros, Alisa Rupenyan
Collection
Published
Control of Multicarrier Energy Systems from Buildings to Networks
Annual Review of Control, Robotics, and Autonomous Systems
Vol 6 No 1 Pages 391-414
Roy Smith, Varsha N. Behrunani, John Lygeros
Doi.org Collection
Published
In-layer Thermal Control of a Multi-layer Selective Laser Melting Process
2022 European Control Conference (ECC)
Pages 1678 - 1683
Dominic Liao-McPherson, Efe Balta, Ryan Wüest, Alisa Rupenyan, John Lygeros
Doi.org Collection
Published
Moving-Horizon State Estimation for Power Networks and Synchronous Generators
arxiv
Milos Katanic, John Lygeros, Gabriela Hug
Doi.org Collection
Published
Incentive-based electric vehicle charging for managing bottleneck congestion
European Journal of Control
Carlo Cenedese, Patrick Stokkink, Nikolas Geroliminis, John Lygeros
Doi.org Collection
Published
On Robustness in Optimization-Based Constrained Iterative Learning Control
IEEE Control Systems Letters
Vol 6 Pages 2846-2851
Dominic Liao-McPherson, Efe Balta, Alisa Rupenyan, John Lygeros
Doi.org Collection
Published
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
EEE Transactions on Industrial Electronics
Mohammad Khosravi, Christopher Koenig, Markus Maier, Roy Smith, John Lygeros, Alisa Rupenyan
Doi.org Collection
Published
Safe Control with Minimal Regret
Proceedings of The 4th Annual Learning for Dynamics and Control Conference
Vol 168 Pages 726-738
Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari Trecate
Doi.org Collection
Published
Learning-Based Repetitive Precision Motion Control with Mismatch Compensation
60th IEEE Conference on Decision and Control (CDC 2021)
Pages 3605-3610
Efe Balta, Kira Barton, Dawn Tilbury, Alisa Rupenyan, John Lygeros
Doi.org Collection
Published
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization
2021 60th IEEE Conference on Decision and Control (CDC)
Pages 2116-2121
Alisa Rupenyan, Mohammad Khosravi, John Lygeros
Doi.org Collection
Published
Performance Bounds of Model Predictive Control for Unconstrained and Constrained Linear Quadratic Problems and Beyond
22nd IFAC World Congress
Vol 56 No 2 Pages 8464-8469
Yuchao Li, Aren Karapetyan, John Lygeros, Karl Johansson, Jonas Martensson
Doi.org Collection
to Appear
Batch Model Predictive Control for Selective Laser Melting
2022 European Control Conference (ECC)
Riccardo Zuliani, Efe Balta, Alisa Rupenyan, John Lygeros
Doi.org Collection
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
IEEE International Conference on Robotics and Automation (ICRA 2021)
Christopher Koenig, Matteo Turchetta, John Lygeros, Alisa Rupenyan, Andreas Krause
Collection
Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
60th IEEE Conference on Decision and Control (CDC21)
Giuseppe Belgioioso, Dominic Liao-McPherson, Mathias Badyn, Saverio Bolognani, John Lygeros, Florian Dörfler
Collection
Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization
IEEE International Conference on Automation Science and Engineering (CASE)
Xavier Guidetti, Alisa Rupenyan, Lutz Fassl, Majid Nabavi, John Lygeros
Collection

Research projects

Title
Principal Investigators

Data-assisted model predictive control with applications to industrial processes

Alisa Rupenyan, John Lygeros

Summary

Manufacturing tasks are often repetitive, involving the same processing steps being repeated over and over to produce identical parts. This makes them well suited for data driven methods that exploit repetition, like the ones developed for autonomous racing. This project addresses deploying the data-assisted predictive control methods in manufacturing applications. The resulting optimisation problems are often of formidable complexity, making them challenging to solve in real time. This calls for efficient solution methods that inherit the stability and performance guarantees of the predictive control design even when the search for the optimal solution has to be terminated prematurely.

People involved

Alisa Rupenyan
Alisa Rupenyan, John Lygeros, Dominic Liao-McPherson
Data-assisted model predictive control with applications to industrial processes
Alisa Rupenyan, John Lygeros

Manufacturing tasks are often repetitive, involving the same processing steps being repeated over and over to produce identical parts. This makes them well suited for data driven methods that exploit repetition, like the ones developed for autonomous racing. This project addresses deploying the data-assisted predictive control methods in manufacturing applications. The resulting optimisation problems are often of formidable complexity, making them challenging to solve in real time. This calls for efficient solution methods that inherit the stability and performance guarantees of the predictive control design even when the search for the optimal solution has to be terminated prematurely.

Alisa Rupenyan
Alisa Rupenyan, John Lygeros, Dominic Liao-McPherson
147
66485d7b-630c-43ea-82da-87c0dd002cf0

DeepGreen: Approximate dynamic programming and reinforcement learning for extremely high dimensional systems

John Lygeros

Summary

The DeepGreen snooker robot project aims to build a robot capable of challenging the best human players. Snooker (similar to billiard) is a game that combines both advanced strategy and physical skills. A single game can be abstractly represented as a zero-sum dynamical game with an extremely highly dimensional state-space (position of each ball, score, and current player), a continuous action space (angle, speed and position of the cue) and nonlinear hybrid dynamics (a combination of the games rules and physics governing the interaction of the cue, the balls and the table). All the above characteristics make snooker the ideal testbed for approximate dynamic programming or reinforcement learning algorithms that have the ambition of filling the “reality gap” and transition from digital simulations to implementation in the real world. The principal objective of the project is the design of the strategy policy for the robotic player. The main challenges are (i) the need of a suitable function approximation scheme for the policy or value function to exploit the characteristics of the game and reduce the dimensionality of the decision problem and (ii) combining the use of data from both a physics engine and the real physical system to reduce the “reality gap” and obtain a strategy that performs well (and improves over time) on the physical robot and not only in a simulated environment.

People involved

Aren Karapetyan
John Lygeros, Aren Karapetyan
DeepGreen: Approximate dynamic programming and reinforcement learning for extremely high dimensional systems
John Lygeros

The DeepGreen snooker robot project aims to build a robot capable of challenging the best human players. Snooker (similar to billiard) is a game that combines both advanced strategy and physical skills. A single game can be abstractly represented as a zero-sum dynamical game with an extremely highly dimensional state-space (position of each ball, score, and current player), a continuous action space (angle, speed and position of the cue) and nonlinear hybrid dynamics (a combination of the games rules and physics governing the interaction of the cue, the balls and the table). All the above characteristics make snooker the ideal testbed for approximate dynamic programming or reinforcement learning algorithms that have the ambition of filling the “reality gap” and transition from digital simulations to implementation in the real world. The principal objective of the project is the design of the strategy policy for the robotic player. The main challenges are (i) the need of a suitable function approximation scheme for the policy or value function to exploit the characteristics of the game and reduce the dimensionality of the decision problem and (ii) combining the use of data from both a physics engine and the real physical system to reduce the “reality gap” and obtain a strategy that performs well (and improves over time) on the physical robot and not only in a simulated environment.

Aren Karapetyan
John Lygeros, Aren Karapetyan
146
c1c6c72e-37b7-455b-b2b3-2e44659c3006

Coupled incentives to ease congestion on the electricity and road networks

John Lygeros, Nikolas Geroliminis

Summary

The electrification of transportation forecast for the near future will couple two problems of similar nature that have been studied independently until now: congestion on the road network and congestion on the electric network. In this project, we will study the viability of coordinated incentives that exploit this coupling to simultaneously ease these congestions more effectively than independently designed incentives.

People involved

Carlo Cenedese
John Lygeros, Nikolas Geroliminis, Carlo Cenedese
Coupled incentives to ease congestion on the electricity and road networks
John Lygeros, Nikolas Geroliminis

The electrification of transportation forecast for the near future will couple two problems of similar nature that have been studied independently until now: congestion on the road network and congestion on the electric network. In this project, we will study the viability of coordinated incentives that exploit this coupling to simultaneously ease these congestions more effectively than independently designed incentives.

Carlo Cenedese
John Lygeros, Nikolas Geroliminis, Carlo Cenedese
118
9505eadd-0857-434d-9fa7-1b5b0a84194c

Intrusion detection and dynamic state estimation for power networks

Gabriela Hug, John Lygeros

Summary

State Estimation is an important tool in the Energy Management systems of electric power systems to monitor the state of the system. It takes measurements from the field and usually by the means of weighted least square optimization identifies the most likely system state. This step is necessary as measurements are subject to measurement errors. Traditionally, the resulting problem formulation includes the non-linear power flow equations. However, in previous work, we have derived a formulation that allows for a linear representation exhibiting the same accuracy but clearly superior computational performance to the non-linear approaches. In this project, we propose to explore how such linear formulation provides computationally efficient means to identify and filter false data injection (FDI) attacks. Protecting the power system against FDI attacks is of critical importance as many control functionalities rely on the reported state of the system.

People involved

NCCR – People
Gabriela Hug, John Lygeros, Milos Katanic
Intrusion detection and dynamic state estimation for power networks
Gabriela Hug, John Lygeros

State Estimation is an important tool in the Energy Management systems of electric power systems to monitor the state of the system. It takes measurements from the field and usually by the means of weighted least square optimization identifies the most likely system state. This step is necessary as measurements are subject to measurement errors. Traditionally, the resulting problem formulation includes the non-linear power flow equations. However, in previous work, we have derived a formulation that allows for a linear representation exhibiting the same accuracy but clearly superior computational performance to the non-linear approaches. In this project, we propose to explore how such linear formulation provides computationally efficient means to identify and filter false data injection (FDI) attacks. Protecting the power system against FDI attacks is of critical importance as many control functionalities rely on the reported state of the system.

NCCR – People
Gabriela Hug, John Lygeros, Milos Katanic
115
656ed7a3-75ba-452e-b0d1-025e9df8b24e

Decentralized control of multi-energy systems

Kristina Orehounig, John Lygeros

Summary

Electrical, thermal and chemical processes follow individual demand and supply patterns under distinct time scales, from sub-second (electrical) to weekly (thermal) and seasonal (chemical) applications. In these applications, digitalization enables a coordination of the related technologies. So, next to sector specific technological limitations, the systemic coupling of energy carriers needs to take into account  the coupling of different time scales as well as different production and consumption patterns. As the technological landscape is getting more decentral in terms of energy production, also more stakeholders are involved in the energetic supply chain. These actors can benefit if they share information on their capabilities and intended production and consumption. Moreover, information sharing and local decision making could potentially reduce the need for extending large-scale infrastructure, such as transmission grids or international imports of energy. The project investigates the use of distributed decision making and control methods to address the large scale nature of the problem, as well as data driven methods to deal with the uncertainty inherent in the problem. Validation of the methodological approach is foreseen on the NEST building at Empa.

People involved

Varsha N. Behrunani
Philipp Heer
Kristina Orehounig, John Lygeros, Varsha N. Behrunani, , Philipp Heer
Decentralized control of multi-energy systems
Kristina Orehounig, John Lygeros

Electrical, thermal and chemical processes follow individual demand and supply patterns under distinct time scales, from sub-second (electrical) to weekly (thermal) and seasonal (chemical) applications. In these applications, digitalization enables a coordination of the related technologies. So, next to sector specific technological limitations, the systemic coupling of energy carriers needs to take into account  the coupling of different time scales as well as different production and consumption patterns. As the technological landscape is getting more decentral in terms of energy production, also more stakeholders are involved in the energetic supply chain. These actors can benefit if they share information on their capabilities and intended production and consumption. Moreover, information sharing and local decision making could potentially reduce the need for extending large-scale infrastructure, such as transmission grids or international imports of energy. The project investigates the use of distributed decision making and control methods to address the large scale nature of the problem, as well as data driven methods to deal with the uncertainty inherent in the problem. Validation of the methodological approach is foreseen on the NEST building at Empa.

Varsha N. Behrunani
Philipp Heer
Kristina Orehounig, John Lygeros, Varsha N. Behrunani, , Philipp Heer
113
34ab7ee3-eae2-4be6-a009-595eb7b858b4

Stochastic behavioural models for data-driven control

Giancarlo Ferrari Trecate, John Lygeros, Florian Dörfler

Summary

Recent results on data-based control exploit alternative parametrizations of linear and nonlinear systems, such as those provided by behavioral theory and deep neural networks. In this project we focus on Input/Output Behavioral (IOB) models that have been used for control by assuming noiseless measurement or deterministic bounded disturbances. The goal of the project is to study the relation between IOB-based control methods and the existing approaches for stochastic systems, including system identification and stochastic model predictive control. New controllers will be tested on the energy systems relevant for the NCCR.

People involved

Giancarlo Ferrari Trecate
Florian Dörfler
Andrea Martin
Giancarlo Ferrari Trecate, John Lygeros, Florian Dörfler, Andrea Martin
Stochastic behavioural models for data-driven control
Giancarlo Ferrari Trecate, John Lygeros, Florian Dörfler

Recent results on data-based control exploit alternative parametrizations of linear and nonlinear systems, such as those provided by behavioral theory and deep neural networks. In this project we focus on Input/Output Behavioral (IOB) models that have been used for control by assuming noiseless measurement or deterministic bounded disturbances. The goal of the project is to study the relation between IOB-based control methods and the existing approaches for stochastic systems, including system identification and stochastic model predictive control. New controllers will be tested on the energy systems relevant for the NCCR.

Giancarlo Ferrari Trecate
Florian Dörfler
Andrea Martin
Giancarlo Ferrari Trecate, John Lygeros, Florian Dörfler, Andrea Martin
111
76577dd2-3aa6-4cf9-a61a-0755301b30cb

Predictive Control for Additive Manufacturing

Alisa Rupenyan, Colin Jones, John Lygeros

Summary

Selective laser melting is an additive manufacturing technology that uses a laser to melt powdered metal into a solid structure based on a sliced 3D CAD model. Currently, such processes are only controlled by setting the process parameters and set points in advance, following multiple process optimisation trials. In this project, process parameters will be optimized with a data-driven closed loop control approach, based on combination of predictive and iterative learning control.

People involved

Alisa Rupenyan
Colin Jones
Alisa Rupenyan, Colin Jones, John Lygeros
Predictive Control for Additive Manufacturing
Alisa Rupenyan, Colin Jones, John Lygeros

Selective laser melting is an additive manufacturing technology that uses a laser to melt powdered metal into a solid structure based on a sliced 3D CAD model. Currently, such processes are only controlled by setting the process parameters and set points in advance, following multiple process optimisation trials. In this project, process parameters will be optimized with a data-driven closed loop control approach, based on combination of predictive and iterative learning control.

Alisa Rupenyan
Colin Jones
Alisa Rupenyan, Colin Jones, John Lygeros
108
4cf5b44b-ef93-4b8a-bf72-5400eacf6831

Framework for industrial control of CPS

Alisa Rupenyan, John Lygeros

Summary

This project is focused on the development of a data-driven framework for self-maintenance and self-optimization for autonomous (robot-assisted) manufacturing. The framework will be characterized by three main components:

  1. Sensing and data processing
  2. Detection of faults and warnings using the extracted features 
  3. Process control and optimization (maintaining safety, optimizing performance

The acquired process data will be used to optimise the manufacturing process by optimising trajectories of the assisting robots, and process parameters, to improve manufacturing productivity.

People involved

Alisa Rupenyan
Jürg P. Keller
Alisa Rupenyan, John Lygeros, Jürg P. Keller
Framework for industrial control of CPS
Alisa Rupenyan, John Lygeros

This project is focused on the development of a data-driven framework for self-maintenance and self-optimization for autonomous (robot-assisted) manufacturing. The framework will be characterized by three main components:

  1. Sensing and data processing
  2. Detection of faults and warnings using the extracted features 
  3. Process control and optimization (maintaining safety, optimizing performance

The acquired process data will be used to optimise the manufacturing process by optimising trajectories of the assisting robots, and process parameters, to improve manufacturing productivity.

Alisa Rupenyan
Jürg P. Keller
Alisa Rupenyan, John Lygeros, Jürg P. Keller
107
74f86789-d97c-4673-a478-7907c8fc7c98

Distributed Dynamic Coverage Control for On-Demand Transportation Operations

Nikolas Geroliminis, Giancarlo Ferrari Trecate, John Lygeros

Summary

Emerging shared-mobility systems create additional opportunities to decrease car ownership and congestion. Asymmetric demand creates imbalances in the distribution of vehicles for these systems. To maximize the covered demand and decrease the waiting time of passengers, vehicle distribution has to be rebalanced with relocations. The potentially very high number of vehicles necessitates using distributed control algorithms to efficiently solve this problem. Presence of multiple companies competing to serve the same demand can be addressed via game theoretic approaches. Μatching algorithms to create shared rides can be combined with the coverage problem to increase coverage in areas with high demand. Fairness in covering low demand areas will also be investigated.

People involved

Giancarlo Ferrari Trecate
Pengbo Zhu
Isik Ilber Sirmatel
Nikolas Geroliminis, Giancarlo Ferrari Trecate, John Lygeros, Pengbo Zhu, , Isik Ilber Sirmatel
Distributed Dynamic Coverage Control for On-Demand Transportation Operations
Nikolas Geroliminis, Giancarlo Ferrari Trecate, John Lygeros

Emerging shared-mobility systems create additional opportunities to decrease car ownership and congestion. Asymmetric demand creates imbalances in the distribution of vehicles for these systems. To maximize the covered demand and decrease the waiting time of passengers, vehicle distribution has to be rebalanced with relocations. The potentially very high number of vehicles necessitates using distributed control algorithms to efficiently solve this problem. Presence of multiple companies competing to serve the same demand can be addressed via game theoretic approaches. Μatching algorithms to create shared rides can be combined with the coverage problem to increase coverage in areas with high demand. Fairness in covering low demand areas will also be investigated.

Giancarlo Ferrari Trecate
Pengbo Zhu
Isik Ilber Sirmatel
Nikolas Geroliminis, Giancarlo Ferrari Trecate, John Lygeros, Pengbo Zhu, , Isik Ilber Sirmatel
105
91547dbb-fb8b-4801-9b24-09f41492e308

Online distributionally robust optimization with streaming data

Florian Dörfler, John Lygeros, Daniel Kuhn

Summary

Recent years have seen a surge of academic and industrial interest in distributionally robust optimization, where the probability distribution of the uncertain problem parameters is itself uncertain and one seeks decisions that are optimal in view of the most adverse distribution within a given ambiguity set. In this project we will study online distributionally robust optimization with streaming data. The key motivation is that dynamic stochastic processes (as encountered in control, estimating, and filtering problems) demand recursive and online solutions with real-time computational constraints. We plan to implement our approaches on various energy system platforms.

People involved

Florian Dörfler
Daniel Kuhn
Soroosh Shafiezadeh Abadeh
Florian Dörfler, John Lygeros, Daniel Kuhn, Soroosh Shafiezadeh Abadeh
Online distributionally robust optimization with streaming data
Florian Dörfler, John Lygeros, Daniel Kuhn

Recent years have seen a surge of academic and industrial interest in distributionally robust optimization, where the probability distribution of the uncertain problem parameters is itself uncertain and one seeks decisions that are optimal in view of the most adverse distribution within a given ambiguity set. In this project we will study online distributionally robust optimization with streaming data. The key motivation is that dynamic stochastic processes (as encountered in control, estimating, and filtering problems) demand recursive and online solutions with real-time computational constraints. We plan to implement our approaches on various energy system platforms.

Florian Dörfler
Daniel Kuhn
Soroosh Shafiezadeh Abadeh
Florian Dörfler, John Lygeros, Daniel Kuhn, Soroosh Shafiezadeh Abadeh
103
eba884a0-fcfd-4541-970c-a9265f02a32d