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Florian Dörfler

Florian Dörfler
Prof. Dr.
Florian Dörfler
PI
Executive Committee Member
ETH Zurich
dorfler@ethz.ch
Curriculum Vitae
NCCR Automation is a unique platform positioning automatic control for the challenges of tomorrow and transferring our methods to timely applications.

Florian Dörfler is an Associate Professor at the Automatic Control Laboratory at ETH Zürich. He received his Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara in 2013, and a Diplom degree in Engineering Cybernetics from the University of Stuttgart in 2008. From 2013 to 2014 he was an Assistant Professor at the University of California Los Angeles. His primary research interests are centered around control, optimization, and system theory with applications in network systems such as electric power grids, robotic coordination, and social networks. He is a recipient of the distinguished young research awards by IFAC (Manfred Thoma Medal 2020) and EUCA (European Control Award 2020). His students have won a healthy number of best paper awards in the prime journals and conferences of control theory, power systems, and circuits & systems.

Scientific Publications

Published
Minimal Regret State Estimation of Time-Varying Systems
Proceedings of IFAC World congress 2023
Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari Trecate
Collection
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
Adaptive real-time grid operation via Online Feedback Optimization with sensitivity estimation
Electric Power Systems Research
Vol 212 Pages 108405
Miguel Picallo, Lukas Ortmann, Saverio Bolognani, Florian Dörfler
Doi.org
Published
Regularization for distributionally robust state estimation and prediction
IEEE Control System Letters
Vol 7 Pages 2713 - 2718
Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari Trecate
Collection
Published
Deployment of an Online Feedback Optimization Controller for Reactive Power Flow Optimization in a Distribution Grid
ArXiv
Lukas Ortmann, Christian Rubin, Alessandro Scozzafava, Janick Lehmann, Saverio Bolognani, Florian Dörfler
Collection Arxiv.org
Published
Virtual Power Grid Reinforcement via Coordinated Volt/VAr Control
ArXiv
Jonas G. Matt, Lukas Ortmann, Saverio Bolognani, Florian Dörfler
Collection Arxiv.org
Published
SOS Construction of Compatible Control Lyapunov and Barrier Functions
Proceedings of IFAC World congress
Michael Schneeberger, Florian Dörfler, Silvia Mastellone
Doi.org Collection
Published
A Self-Contained Karma Economy for the Dynamic Allocation of Common Resources
Dynamic Games and Applications
Ezzat Elokda, Saverio Bolognani, Andrea Censi, Florian Dörfler, Emilio Frazzoli
Collection
Published
Factorization of Dynamic Games over Spatio-Temporal Resources
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Pages 13159 - 13166
Alessandro Zanardi, Saverio Bolognani, Andrea Censi, Florian Dörfler, Emilio Frazzoli
Doi.org
Published
Stochastic Wasserstein Gradient Flows using Streaming Data with an Application in Predictive Maintenance
ArXiv
Nicolas Lanzetti, Efe Balta, Dominic Liao-McPherson, Florian Dörfler
Collection
Published
Real-time Curative Actions for Power Systems via Online Feedback Optimization
ArXiv
Lukas Ortmann, Gianni Hotz, Saverio Bolognani, Florian Dörfler
Doi.org Collection
Published
Robust online joint state/input/parameter estimation of linear systems
2022 IEEE 61st Conference on Decision and Control (CDC)
Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler, Giancarlo Ferrari Trecate
Doi.org Collection
Published
Real-time Projected Gradient-based Nonlinear Model Predictive Control with an Application to Anesthesia Control
CDC 2022
Sophie Hall, Lukas Ortmann, Miguel Picallo, Florian Dörfler
Doi.org Collection
Published
Real-Time Feasibility of Data-Driven Predictive Control for Synchronous Motor Drives
IEEE Transactions on Power Electronics
Vol 38 No 2 Pages 1672
Paolo Gherardo Carlet, Andrea Favato, Riccardo Torchio, Francesco Toso, Saverio Bolognani, Florian Dörfler
Doi.org
Published
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
Proceedings of, the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Antonio Terpin, Nicolas Lanzetti, Batuhan Yardim, Florian Dörfler, Giorgia Ramponi
Doi.org Collection
Published
Bayesian Error-in-Variables Models for the Identification of Distribution Grids
IEEE Transactions on Smart Grid
Vol 14 No 2
Jean-Sébastien Brouillon, Emanuele Fabbiani, Pulkit Nahata, Keith Moffat, Florian Dörfler, Giancarlo Ferrari Trecate
Collection
to Appear
Receding Horizon Games with Coupling Constraints for Demand-Side Management
61st IEEE Conference on Decision and Control (CDC 2022)
Sophie Hall, Giuseppe Belgioioso, Dominic Liao-McPherson, Florian Dörfler
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
Bayesian Methods for the Identification of Distribution Networks
2021 60th IEEE Conference on Decision and Control (CDC)
Pages 3646-3651
Jean-Sébastien Brouillon, Emanuele Fabbiani, Pulkit Nahata, Florian Dörfler, Giancarlo Ferrari Trecate
Doi.org Collection
Emergence of Zipf's law among social networks influencers
Extended Abstract IFAC Workshop on Networked Systems
Nicolò Pagan, Wenjun Mei, Florian Dörfler
Published
Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics
IEEE Robotics and Automation Letters
Vol 7 No 2 Pages 1292-1299
Alessandro Zanardi, Gioele Zardini, Sirish Srinivasan, Saverio Bolognani, Andrea Censi, Florian Dörfler, Emilio Frazzoli
Doi.org Collection
Published
Game Theory to Study Interactions between Mobility Stakeholders
24th IEEE International Conference on Intelligent Transportation Systems (ITSC)
Pages 2054-2061
Gioele Zardini, Nicolas Lanzetti, Laura Guerrini, Emilio Frazzoli, Florian Dörfler
Doi.org Collection
Published
Cross-layer design for real-time grid operation: Estimation, optimization and power flow
Electric Power Systems Research
Vol 212 No 108378
Miguel Picallo, Dominic Liao-McPherson, Saverio Bolognani, Florian Dörfler
Collection
Published
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models
Conference on Neural Information Processing Systems (NeurIPS 2021)
Pages 29780 - 29793
Lenart Treven, Philippe Wenk, Florian Dörfler, Andreas Krause
Doi.org Collection
Published
Sensitivity Conditioning: Beyond Singular Perturbation for Control Design on Multiple Time Scales
IEEE Transactions on Automatic Control
Vol 68 No 4 Pages 2309
Miguel Picallo, Saverio Bolognani, Florian Dörfler
Doi.org
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
Conference on Neural Information Processing Systems (NeurIPS 2021)
Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dörfler
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

Research projects

Title
Principal Investigators

Online estimation and control for autonomous electric networks

Giancarlo Ferrari Trecate, Florian Dörfler

Summary

For temporally varying electric distribution networks, reliable information about the system topology and parameters may be missing or outdated. In the project we will develop online estimation algorithms for the network reconstruction, so as to automatically track changes, even in the presence of noisy and incomplete data. Furthermore, we will blend online estimation algorithms with higher-level controllers commonly used in distribution networks  and apply our methods to the moon-shot testbed developed in the NCCR. Our ultimate goal is to contribute to the development of autonomous electric networks that self-adapt in real-time to changing ambient conditions.

People involved

Giancarlo Ferrari Trecate
Florian Dörfler
Jean-Sébastien Brouillon
Giancarlo Ferrari Trecate, Florian Dörfler, Jean-Sébastien Brouillon
Online estimation and control for autonomous electric networks
Giancarlo Ferrari Trecate, Florian Dörfler

For temporally varying electric distribution networks, reliable information about the system topology and parameters may be missing or outdated. In the project we will develop online estimation algorithms for the network reconstruction, so as to automatically track changes, even in the presence of noisy and incomplete data. Furthermore, we will blend online estimation algorithms with higher-level controllers commonly used in distribution networks  and apply our methods to the moon-shot testbed developed in the NCCR. Our ultimate goal is to contribute to the development of autonomous electric networks that self-adapt in real-time to changing ambient conditions.

Giancarlo Ferrari Trecate
Florian Dörfler
Jean-Sébastien Brouillon
Giancarlo Ferrari Trecate, Florian Dörfler, Jean-Sébastien Brouillon
112
cf878966-5241-4f6e-bb36-4f46dbfe98d6

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

Data-driven control and estimation for the next generation of plug-and-play power converters

Silvia Mastellone, Florian Dörfler

Summary

The project aims at developing an autonomous and reconfigurable control architecture where each converter operates in plug-and-play mode, i.e., without additional communication overhead or detailed model knowledge, and under variable system conditions. This requires in a first step that each converter estimates online the equivalent circuit as seen at its terminals. Based on such estimates, the converter controller reconfigures its structure and adapts online its parameters in order to optimally perform under the different grid configurations and operating conditions.

People involved

Silvia Mastellone
Florian Dörfler
Michael Schneeberger
Silvia Mastellone, Florian Dörfler, Michael Schneeberger
Data-driven control and estimation for the next generation of plug-and-play power converters
Silvia Mastellone, Florian Dörfler

The project aims at developing an autonomous and reconfigurable control architecture where each converter operates in plug-and-play mode, i.e., without additional communication overhead or detailed model knowledge, and under variable system conditions. This requires in a first step that each converter estimates online the equivalent circuit as seen at its terminals. Based on such estimates, the converter controller reconfigures its structure and adapts online its parameters in order to optimally perform under the different grid configurations and operating conditions.

Silvia Mastellone
Florian Dörfler
Michael Schneeberger
Silvia Mastellone, Florian Dörfler, Michael Schneeberger
104
de9d6331-cbcb-4b12-b9e4-9c4f6fe448c9

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

Online Feedback Optimization with Self-Interested Agents for Energy Management Applications

Florian Dörfler

Summary

Online feedback optimization refers to the design of feedback controllers that asymptotically steer a physical system to the solution of an optimization problem while respecting physical and operational constraints. Here we are interested in exploring self-interested agents that do not want to cooperate for the sake of achieving a common goal but first and foremost have their own interest in mind. A relevant real-world example is selfish and uncoordinated congestion control by different power transmission system operators. We will investigate distributed Nash-seeking algorithms to solve the resulting antagonistic decision-making problems, and also deploy them in numerical and real-world case studies.

People involved

Florian Dörfler
NCCR – People
Florian Dörfler, Giuseppe Belgioioso, , Saverio Bolognani
Online Feedback Optimization with Self-Interested Agents for Energy Management Applications
Florian Dörfler

Online feedback optimization refers to the design of feedback controllers that asymptotically steer a physical system to the solution of an optimization problem while respecting physical and operational constraints. Here we are interested in exploring self-interested agents that do not want to cooperate for the sake of achieving a common goal but first and foremost have their own interest in mind. A relevant real-world example is selfish and uncoordinated congestion control by different power transmission system operators. We will investigate distributed Nash-seeking algorithms to solve the resulting antagonistic decision-making problems, and also deploy them in numerical and real-world case studies.

Florian Dörfler
NCCR – People
Florian Dörfler, Giuseppe Belgioioso, , Saverio Bolognani
102
7b2b32e9-400f-4978-84c3-7e7316f1c773

Optimization Flows on Probability Distribution Spaces

Florian Dörfler

Summary

We want to develop continuous-time optimality-seeking algorithms on the space of probability distributions. We envision our approach to result in practically useful algorithms to probabilistic (e.g. distributionally robust) optimization problems. Furthermore, our framework is directly applicable to optimization and control of traffic flow in a density modelling approach or a continuum modelling of multi-agent systems.

People involved

Florian Dörfler
NCCR – People
Florian Dörfler, Nicolas Lanzetti, , Saverio Bolognani
Optimization Flows on Probability Distribution Spaces
Florian Dörfler

We want to develop continuous-time optimality-seeking algorithms on the space of probability distributions. We envision our approach to result in practically useful algorithms to probabilistic (e.g. distributionally robust) optimization problems. Furthermore, our framework is directly applicable to optimization and control of traffic flow in a density modelling approach or a continuum modelling of multi-agent systems.

Florian Dörfler
NCCR – People
Florian Dörfler, Nicolas Lanzetti, , Saverio Bolognani
101
bcd7d27a-de53-49f7-949b-9a48b4ec7eb6

Dynamic population games for efficient autonomous mobility

Florian Dörfler, Emilio Frazzoli

Summary

We will demonstrate that multiple competitive agents can efficiently share a mobility infrastructure without the need for an external coordinator. Standard game-theoretic approaches to this problem fall short in case of dynamic systems as encountered in autonomous mobility, coordinated use of the mobility space, traffic congestion control, etc.  We will develop a new mathematical formalism and computational methods blending the concept of game-theoretic and dynamic equilibria. Autonomous mobility is an important application due to the importance of fairness and efficiency in resource use, the large number of interacting agents, and the need for automated and scalable solutions.

People involved

Florian Dörfler
Emilio Frazzoli
Ezzat Elokda
Florian Dörfler, Emilio Frazzoli, Ezzat Elokda, , Saverio Bolognani, , Andrea Censi
Dynamic population games for efficient autonomous mobility
Florian Dörfler, Emilio Frazzoli

We will demonstrate that multiple competitive agents can efficiently share a mobility infrastructure without the need for an external coordinator. Standard game-theoretic approaches to this problem fall short in case of dynamic systems as encountered in autonomous mobility, coordinated use of the mobility space, traffic congestion control, etc.  We will develop a new mathematical formalism and computational methods blending the concept of game-theoretic and dynamic equilibria. Autonomous mobility is an important application due to the importance of fairness and efficiency in resource use, the large number of interacting agents, and the need for automated and scalable solutions.

Florian Dörfler
Emilio Frazzoli
Ezzat Elokda
Florian Dörfler, Emilio Frazzoli, Ezzat Elokda, , Saverio Bolognani, , Andrea Censi
100
25d61593-91d6-4a68-b2c0-4b5078f9de13

Continuous-time Deep Model-based Reinforcement Learning

Florian Dörfler, Andreas Krause

Summary

We investigate how classical methods from system identification, dynamical systems, and control can be combined with techniques from non- parametric machine learning to develop a rigorous framework for continuous-time model-based deep reinforcement learning. Concretely, we will learn a continuous-time dynamics model via neural ordinary differential equations, along with associated uncertainty bounds that can be used to trade exploration and exploitation. Ultimately, our framework should enable control design for complex and uncertain systems, and we plan to apply it to problems in building automation and grid-connected converters.

People involved

Florian Dörfler
Andreas Krause
Lenart Treven
Florian Dörfler, Andreas Krause, Lenart Treven
Continuous-time Deep Model-based Reinforcement Learning
Florian Dörfler, Andreas Krause

We investigate how classical methods from system identification, dynamical systems, and control can be combined with techniques from non- parametric machine learning to develop a rigorous framework for continuous-time model-based deep reinforcement learning. Concretely, we will learn a continuous-time dynamics model via neural ordinary differential equations, along with associated uncertainty bounds that can be used to trade exploration and exploitation. Ultimately, our framework should enable control design for complex and uncertain systems, and we plan to apply it to problems in building automation and grid-connected converters.

Florian Dörfler
Andreas Krause
Lenart Treven
Florian Dörfler, Andreas Krause, Lenart Treven
99
2d302258-eb79-48e4-a277-d88660d68bfa