Saverio Bolognani

Dr.
Saverio Bolognani
Principal Investigators
Management
Feedback is the principle behind efficient interaction as well as cascading failures. We need to understand feedback to understand autonomous systems.

Saverio Bolognani received the Ph.D. degree in Information Engineering from the University of Padova, Italy, in 2011. In 2013 – 2014 he was a Postdoctoral Associate at the Laboratory for Information and Decision Systems of the Massachusetts Institute of Technology in Cambridge (MA). He is currently a Senior Researcher at the Automatic Control Laboratory at ETH Zurich. His research interests include the application of control system theory to power systems, distributed control, online optimization, cyber-physical systems, and game theory for multi-agent autonomous systems.

Scientific Publications

Published
Adaptive real-time grid operation via Online Feedback Optimization with sensitivity estimation
Electric Power Systems Research
Vol 212 Pages 108405
Published
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games
IEEE Robotics and Automation Letters
Vol 8 No 4 Pages 2293
Published
Deployment of an Online Feedback Optimization Controller for Reactive Power Flow Optimization in a Distribution Grid
ArXiv
Published
Virtual Power Grid Reinforcement via Coordinated Volt/VAr Control
ArXiv
Published
A Self-Contained Karma Economy for the Dynamic Allocation of Common Resources
Dynamic Games and Applications
Published
Factorization of Dynamic Games over Spatio-Temporal Resources
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Pages 13159 - 13166
Published
Real-time Curative Actions for Power Systems via Online Feedback Optimization
ArXiv
Published
Real-Time Feasibility of Data-Driven Predictive Control for Synchronous Motor Drives
IEEE Transactions on Power Electronics
Vol 38 No 2 Pages 1672
Published
A Dynamic Population Model of Strategic Interaction and Migration under Epidemic Risk
2021 60th IEEE Conference on Decision and Control (CDC)
Pages 2085-2091
Published
Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression
Journal of Process Control
Vol 121 No 119
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
Published
Cross-layer design for real-time grid operation: Estimation, optimization and power flow
Electric Power Systems Research
Vol 212 No 108378
Published
Sensitivity Conditioning: Beyond Singular Perturbation for Control Design on Multiple Time Scales
IEEE Transactions on Automatic Control
Vol 68 No 4 Pages 2309
Game Theoretical Motion Planning - Tutorial session
IEEE International Conference on Robotics and Automation (ICRA)
Urban Driving Games With Lexicographic Preferences and Socially Efficient Nash Equilibria
IEEE Robotics and Automation Letters
Vol 6 No 3 Pages 4978 – 4985
Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking
60th IEEE Conference on Decision and Control (CDC21)

Research projects as Researcher

Title
Principal Investigators

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

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.

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

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.

102
7b2b32e9-400f-4978-84c3-7e7316f1c773

Optimization Flows on Probability Distribution Spaces

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.

Optimization Flows on Probability Distribution Spaces

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.

101
bcd7d27a-de53-49f7-949b-9a48b4ec7eb6

Dynamic population games for efficient autonomous mobility

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.

Dynamic population games for efficient autonomous mobility

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.

100
25d61593-91d6-4a68-b2c0-4b5078f9de13

Attended events

News & Insights

11-01-2024
sustainable automation
NCCR Automation researchers from the Automatic Control Laboratory, ETH Zurich and the electricity supplier AEW Energie AG have won the 2024 Watt d’Or award in the Energy Technologies category. Their algorithm, which was implemented at AEW Energie, makes it possible to optimise electricity grid operations.
24-04-2023
At a time when countries around the world are threatened with energy shortages, ETH Zurich and NCCR Automation researchers have developed a high-impact, low-cost software solution to improve resilience and efficiency. This innovative approach enables providers to make the most of decentralized energy resources such as photovoltaic systems through flexible, responsive, and data-driven operation.