SA/BA/MA projects

Have you heard of industry 4.0, smart grids, smart buildings, smart cities, intelligent traffic systems and intelligent self-driving cars? Did you ever wonder what makes them smart and intelligent?

The answer is control and automation and that’s what we do at the Automatic Control Laboratory (IfA). We use control theory, optimization, machine learning, and game theory to develop controllers and algorithms that are the backbone of nearly all modern technology. At our lab we span the whole area from pure theory to real-world applications and we are looking for you to help us push forward the state of the art. If you want to learn techniques and gain knowledge that enable you to work in any field from medical applications to spacecraft and from electrical grids to finance then the Automatic Control Laboratory is the place for you!

Lists of currently running and recently completed projects can be found in the sub-navigation menu above. Reports for several projects are available through the ETHZ Research Collection:

Open projects

A Game Plan for Electricity: Optimal Incentive Design for Procuring Voltage Control in Transmission Grids

The reliable operation of modern power systems increasingly relies on flexible and distributed resources such as renewable generators, battery storage, and demand-side participants. These resources can provide essential control services such as frequency and voltage support. As many of these resources participate voluntarily, system operators must design incentive mechanisms that encourage participation while ensuring safe and stable grid operation. Existing approaches for procuring and coordinating such services often lack formal guarantees regarding stability, robustness, and economic efficiency, especially under uncertainty in system dynamics, participant behavior, or external disturbances. This creates an opportunity to develop new incentive and control strategies that combine theoretical rigor with practical applicability.

Keywords

Incentive Design, Game Theory, Power Grid, Energy Systems, Swissgrid, Voltage, Control, Optimization

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-10-29

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts Matt Jonas

Topics Mathematical Sciences , Engineering and Technology

Reading the Game: Predicting the Behavior of Participants in Swissgrid’s Voltage Control Program via Inverse Optimization and Inverse Game Theory

The secure and efficient operation of the Swiss transmission grid relies on the cooperation of connected agents, such as power plants and distribution system operators (DSOs), which can provide voltage support through reactive power control. To promote this behavior, Swissgrid offers financial incentives via its voltage support program. However, operational data shows that participants react very heterogeneously to these incentives. The reasons for this diversity are unclear, as the agents’ cost structures, technical limits, and strategic motivations are not directly observable. Understanding this behavioral diversity is key to designing more effective and equitable incentive mechanisms for future power systems.

Keywords

Inverse, Optimization, Game Theory, Control, Incentive Design, Learning, Stackelberg, Leader, Follower, Bilevel, Swissgrid, Power Grid, Energy Systems

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Semester Project , Bachelor Thesis , Master Thesis

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Published since: 2025-10-29

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts Matt Jonas

Topics Mathematical Sciences , Engineering and Technology

Enhancing Stability of Large-Scale Power Systems via Learning Dissipativity

Modern power systems are nonlinear, complex, and interconnected with numerous heterogeneous components, causing significant challenges to system stability. Control theory techniques that provide stability guarantees typically rely on a simplified model and do not capture the nonlinear behavior of the dynamics, motivating a deep-learning-based approach. However, naive deep-learning-based approaches generally suffer from the scale of dimensionality, especially in the context of large-scale power systems. Therefore, this project aims to develop a deep learning-based controller in a decentralized fashion based on dissipativity theory, in order to ensure the global stability of the system in a scalable fashion.

Keywords

Deep Learning, Interconnected System, Power System Transient Stability

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-10-24 , Earliest start: 2025-10-27

Organization Automatic Control Laboratory

Hosts Wang Han , Nakano Taiki

Topics Engineering and Technology

Small-Signal Data-Driven Modeling of Power Converters Under Unbalanced Conditions

This project focuses on developing and validating data-driven small-signal models of grid-connected converters under unbalanced and harmonic conditions. The identified models are crucial to understand sub-synchronous oscillations caused by converter-grid interactions.

Keywords

Data-driven modelling, System Identification, Power Converters, Small-signal modelling, Sub-synchronous oscillations, Control, Stability, Unbalanced, Harmonics

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Master Thesis

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Power grids are undergoing a fundamental transformation due to the ever-increasing integration of power-electronics-based resources. This high penetration profoundly changes the dynamic behavior of the system, creating a complex and fast timescale interplay among various subsystems of the grid. Consequently, accurate modeling is paramount to ensure stable and optimal grid operation. However, a significant challenge exists: traditional small-signal models of power-electronics converters frequently rely on simplifying assumptions (e.g., balanced three-phase operation). This limitation often restricts their applicability and accuracy in real-world operating scenarios. To address this, recent advances in data-driven modeling have proven highly valuable. Data-driven modeling effectively captures the essential small-signal dynamics of converter-grid interactions without requiring complex, fully detailed analytical models. This enables systematic and accurate analysis of stability as well as control tuning under practical, real-world grid conditions. This project focuses on developing and validating data-driven small-signal models of grid-connected converters under unbalanced and harmonic conditions. The identified models will be used to analyze the stability of interconnected systems and assess how unbalanced operation may affect the system's dynamics. Depending on the student's interest, we can help to publish the results in a conference paper. **Prerequisites** Competitive candidates will have a background in power systems/electronics, systems & control, and/or signal processing. A prior experience or coursework related to grid-connected converters or system identification would be highly beneficial. Proficiency in MATLAB/Simulink (or similar power system simulation tools) is required to implement and test the methods. **References** 1. Shi, Jinzhu, et al. "Impedance modeling and quantitative stability analysis of grid-connected voltage source converters under complex unbalanced conditions." Electric Power Systems Research 238 (2025): 111084. 2. Tayyebi, Ali, et al. "A generalized time-domain framework for modeling and analysis of the unbalanced three-phase systems." Electric Power Systems Research 235 (2024): 110809. 3. Y. Han et al., "Modeling of Grid-Connected Converters Under Unbalanced and Harmonic Conditions." IEEE Transactions on Power Electronics, vol. 36, no. 11, pp. 12 345–12 356, 2021. 4. Mollerstedt, Erik, and Bo Bernhardsson."Out of control because of harmonics-an analysis of the harmonic response of an inverter locomotive." IEEE Control Systems Magazine 20.4 (2002): 70-81.

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Published since: 2025-10-22

Organization Automatic Control Laboratory

Hosts Abdalmoaty Mohamed

Topics Engineering and Technology

Multi-Agent Data-Driven Control for Power Oscillation Damping

This project investigates multi-agent data-driven control as a novel approach to damping oscillations in converter-dominated power systems, where classical model-based methods are increasingly unreliable. The study will compare a centralized benchmark with decentralized schemes, assessing how locally updated controllers can collectively achieve coordinated, system-wide stability.

Keywords

data-driven control, multi-agent system, dynamic power system modelling

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Master Thesis

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Published since: 2025-10-02 , Earliest start: 2026-01-01

Organization Automatic Control Laboratory

Hosts Häberle Verena

Topics Engineering and Technology

Multi-Agent Grid Impedance Identification in Three-Phase Power Systems

This project investigates multi-agent grid impedance identification in three-phase power systems. It will develop and compare two approaches: a global multi-port identification framework and a locally simultaneous multi-agent single-port identification framework. The study will evaluate and compare the accuracy of the two approaches and explore potential downstream applications in stability analysis and control design.

Keywords

grid impedance, system identification, multi-agent systems, three-phase power system dynamic modelling

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Master Thesis

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Published since: 2025-10-01 , Earliest start: 2026-01-01

Organization Automatic Control Laboratory

Hosts Häberle Verena

Topics Engineering and Technology

Optimal Excitation for Grid Impedance Estimation

This project aims to develop optimal excitation schemes for impedance estimation of grid/grid-connected converters.

Keywords

Impedance estimation; grid-connected converters; optimal excitation; experiment design; system identification

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Master Thesis

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The use of renewable energy sources and distributed generation, such as wind turbines and solar power plants, in modern power systems has been increasing significantly. This resulted in a heightened use of power-electronics-based converters, changing the dynamical behaviour of the power grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. However, due to the grid's dynamic and nonlinear nature, impedance estimation is particularly challenging (see e.g., [2]). The grid-connected converter's controller must maintain stable operation while simultaneously allowing the injection of small excitation signals necessary for grid impedance estimation. This creates a fundamental trade-off: while a good controller and converter-grid interface filter improve system stability and disturbance rejection, they can also suppress the grid's response to the excitation, reducing the estimation accuracy. On the other hand, injecting larger excitation signals could improve the quality of estimation but risks triggering the converter protection system and/or exciting nonlinear grid behaviors. Moreover, it is not clear what the best class of excitation signals is nor the best injection point within the converter controller. This project aims to address this trade-off and questions by developing optimal excitation schemes for grid impedance estimation using grid-connected converters. A main goal is to obtain a good signal-to-noise ratio at the frequencies of interest while respecting experimental time and signal magnitude constraints. A class of excitation signals of potential interest is random phase multisines [3]. Recent related results in the literature are [4,5] which are based on modelling the noise and the use of multiple sinusoidal injections. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] Godfrey, Keith, ed. Perturbation signals for system identification. Prentice Hall International, 1993. [4] Y. Zhu, et.al, "Injection Amplitude Guidance for Impedance Measurement in Power Systems," in IEEE Transactions on Power Electronics, vol. 38, no. 6, June 2023 [5] H. Li, et.al., "A Noise Interference Suppression Method With Optimized Frequency Adjustment for Impedance Measurement," in IEEE Transactions on Industrial Electronics, vol. 71, no. 12, Dec. 2024 **Prerequisits** We are looking for a motivated student with excellent written and verbal communication skills, and the ability to learn and problem-solve independently. Competitive candidate will have a background in systems & control, signal processing, and power systems/electronics. A prior experience or coursework in system identification and/or grid-connected converters would be highly beneficial. Proficiency in MATLAB/Simulink or a similar simulation environment is required for implementing and testing the methods.

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Published since: 2025-09-22 , Earliest start: 2025-09-21

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts He Xiuqiang , Abdalmoaty Mohamed

Topics Engineering and Technology

Adaptive control via reinforcement learning: stability, optimality, and robustness

This project explores reinforcement learning (RL) for adaptive control of linear time-invariant systems, with a focus on achieving stability, optimality, and robustness. While RL-based adaptive control methods are gaining popularity, most lack rigorous stability guarantees, especially when applied to the linear quadratic regulator (LQR) problem. Building on recent advances in sequential stability analysis, the project aims to develop RL algorithms that ensure closed-loop stability and convergence to the optimal LQR policy. Theoretical insights will be validated through simulations on representative control systems.

Keywords

data-driven control, adaptive control, reinforcement learning, linear time-invariant system

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-09-09 , Earliest start: 2025-09-07 , Latest end: 2026-07-01

Organization Automatic Control Laboratory

Hosts Bartos Marcell , Zhao Feiran

Topics Mathematical Sciences , Information, Computing and Communication Sciences

Direct Data-Driven and Adaptive Control for Nonlinear Systems

This project investigates direct data-driven adaptive control for nonlinear systems by extending the recently developed Data-enabled Policy Optimization (DeePO) framework. DeePO provides stability, optimality, and robustness guarantees in linear settings, but its potential for nonlinear dynamics remains largely unexplored. By integrating DeePO with modern techniques for cancelling nonlinearities, the project seeks to design controllers that both stabilize the system and systematically compensate for nonlinear effects, yielding a simplified closed loop that can be rigorously analyzed. The student will develop algorithms, establish closed-loop guarantees, and validate the methods through simulations and real-world experiments on benchmark systems.

Keywords

Adaptive control; Data-driven control; Nonlinear control; Reinforcement learning

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Master Thesis

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Published since: 2025-09-09 , Earliest start: 2025-09-10 , Latest end: 2026-05-01

Organization Automatic Control Laboratory

Hosts Zhao Feiran , Wang Han

Topics Mathematical Sciences , Information, Computing and Communication Sciences

Safe and Reliable Model Predictive Control using Differentiable Optimization

Safety violations in control systems can lead to catastrophic outcomes, from autonomous vehicle crashes to power grid failures. While Model Predictive Control (MPC) offers powerful safety mechanisms through constraint enforcement, a critical dilemma emerges: improved controller performance often comes at the expense of safety margins. Traditional tuning approaches that prioritize performance metrics may inadvertently compromise safety guarantees. This project addresses this fundamental challenge by developing a tuning framework that enhances MPC performance while providing anytime safety guarantees—ensuring the system remains safe even during ongoing optimization. The approach offers a principled solution for deploying high-performance, safety-critical control in autonomous systems, robotics, and industrial processes.

Keywords

Model Predictive Control, Learning-based Control, Differentiable Optimization

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Master Thesis

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_Note_: please read the attached PDF for a better reading experience. Safety is a critical concern in control applications, particularly in systems where constraint violations can lead to hazardous outcomes, such as in autonomous driving, robotics, or process control. Model Predictive Control (MPC) offers a powerful framework to ensure safety by explicitly accounting for system constraints over a receding prediction horizon. By leveraging an internal model of the system and repeatedly solving a constrained optimization problem online, MPC can proactively enforce safety limits on states and inputs, while anticipating future behavior. This predictive capability enables MPC to integrate safety requirements into the control synthesis process, making it a suitable tool for safety-critical applications, as highlighted in recent works emphasizing formal guarantees and robust constraint satisfaction under uncertainty. MPC performance can be significantly enhanced through hyperparameter optimization techniques, which systematically tune parameters such as constraints and cost weights to better suit specific control tasks. As demonstrated in recent work [1], such optimization can lead to improved closed-loop performance by balancing control accuracy and computational complexity. These methods move beyond manual trial-and-error tuning, offering scalable and automated strategies to deploy high-performance MPC in real-world applications. While hyperparameter optimization can significantly enhance the performance of MPC controllers, it may also introduce safety risks if not carefully constrained. Automated tuning methods often focus on improving performance metrics such as tracking accuracy or economic cost, potentially overlooking critical safety constraints or robustness margins. For instance, optimizing cost weights without explicitly accounting for constraint satisfaction can result in aggressive control actions that violate system limits or reduce stability margins (see the example in Figure 2). Therefore, it is crucial to integrate safety-aware mechanisms into the optimization process, to ensure that performance improvements do not come at the expense of system safety.

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Published since: 2025-09-02 , Earliest start: 2025-07-01

Organization Automatic Control Laboratory

Hosts Zuliani Riccardo

Topics Engineering and Technology

High-performance Model Predictive Control for Autonomous Driving via Policy Optimization, in Collaboration with AMZ

Autonomous driving demands controllers that combine high performance, safety, and real-time feasibility. While Model Predictive Control (MPC) meets these requirements in principle, practical implementations often rely on simplified models and short horizons, leading to suboptimal performance. This project aims to develop a hyperparameter tuning scheme that optimizes MPC parameters to recover near-optimal behavior without increasing model complexity or prediction horizon. The method will be validated in simulation and, for interested students, tested on an autonomous racing car, aiming to reduce lap times while ensuring compliance with safety constraints such as tire friction and lane boundaries.

Keywords

Autonomous Driving, Model Predictive Control, Learning-based Control

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Semester Project , Master Thesis

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Published since: 2025-09-02 , Earliest start: 2025-09-14

Organization Automatic Control Laboratory

Hosts Hoteit Rawan , Zuliani Riccardo

Topics Engineering and Technology

Semantic Segmentation for Volume Estimation

This thesis investigates the use of vision foundation models for semantic segmentation within 3D reconstruction pipelines to improve volume estimation in industrial settings. Using multi-view datasets from Tinamu Labs, the work focuses on segmenting stockpiles, static warehouse structures, and occluding objects. The approach combines geometric information with segmentation models and addresses occluded or missing regions through automatic detection and infill. The outcome supports more accurate and robust volume estimation, contributing to automated inventory management. The project is conducted in collaboration with Tinamu Labs and validated on their robotic systems.

Keywords

Data analysis, machine learning, semantic segmentation

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Master Thesis

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Published since: 2025-09-01 , Earliest start: 2025-09-15 , Latest end: 2025-12-19

Organization Automatic Control Laboratory

Hosts Zakwan Muhammad

Topics Engineering and Technology

Dynamic Kicking Skills for Humanoid Robots

Powerful yet accurate kicking is an essential skill for humanoid robots, especially in the context of robotic soccer competitions like RoboCup. Developing effective kicking skills for humanoid robots is a complex task that involves a combination of mechanical design, advanced control algorithms, and sensor integration. This project aims to develop a robust control system for a NAO robot to successfully kick a rolling ball into a designated goal area while dynamically tracking the ball position.

Keywords

Motion control, Reference Tracking, Robotics

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Semester Project

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Published since: 2025-08-13 , Earliest start: 2025-09-16 , Latest end: 2026-03-31

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts Hoteit Rawan

Topics Engineering and Technology

Modeling a tri-winged airborne wind turbine, a data-driven approach

Wind energy is key to the green transition, but traditional turbines are costly and long to build. Airborne Wind Energy (AWE) offers a lighter, cheaper alternative by using tethered wings to harness stronger winds at higher altitudes. We have developed a novel AWE system with three interconnected wings orbiting each other—early results show it’s not only easier and cheaper to build but also offers better control than current AWE designs. We need an accurate and robust dynamic model of the system. In this project, the student will use system identification techniques to derive models of the three-wing AWE system. You will work with both simulation data and measurements from a small-scale prototype, with the goal of delivering a validated identification pipeline that will be tested on a larger prototype at the end of the project.

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-07-28 , Earliest start: 2025-09-07 , Latest end: 2026-04-30

Organization Automatic Control Laboratory

Hosts Brouillon Jean-Sébastien

Topics Engineering and Technology

Modeling a tri-winged airborne wind turbine, first principles

Wind energy is key to the green transition, but traditional turbines are costly and long to build. Airborne Wind Energy (AWE) offers a lighter, cheaper alternative by using tethered wings to harness stronger winds at higher altitudes. We have developed a novel AWE system with three interconnected wings orbiting each other—early results show it’s not only easier and cheaper to build but also offers better control than current AWE designs. Before control strategies, safety validations, and certifications can be addressed, we need an accurate and robust dynamic model of the system. In this project, the student will use theoretical first principles from fluid dynamics to derive good model candidates, in increasing levels of detail and complexity. Initial values of the model parameters should be provided based on airfoil data and/or computational fluid dynamics.

Keywords

Green energy, aeronautics, dynamical models

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-07-28 , Earliest start: 2025-09-07 , Latest end: 2026-04-30

Organization Automatic Control Laboratory

Hosts Brouillon Jean-Sébastien

Topics Engineering and Technology

Disturbance rejection for a tri-winged airborne wind turbine

Wind energy is key to the green transition, but traditional turbines are costly and long to build. Airborne Wind Energy (AWE) offers a lighter, cheaper alternative by using tethered wings to harness stronger winds at higher altitudes. We have developed a novel AWE system with three interconnected wings orbiting each other—early results show it’s not only easier and cheaper to build but also offers better control than current AWE designs. While our system is passively stable, its sensitivity to disturbances from wind gusts and other sources must be quantified to obtain the required safety margins. Moreover, several active control architectures will be explored to reduce this sensitivity as much as possible. You will work with both a comprehensive simulation framework and a small-scale prototype, with the goal of delivering a disturbance sensitivity analysis that will be tested in-field on a larger prototype at the end of the project. This thesis is part of the foundational work for a startup aiming to bring this innovative concept into real-world applications.

Keywords

Green energy, aeronautics, control systems

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Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-07-28 , Earliest start: 2025-09-07 , Latest end: 2026-04-30

Organization Automatic Control Laboratory

Hosts Brouillon Jean-Sébastien

Topics Engineering and Technology

Regulatory framework for airborne wind energy systems

Keywords

Green energy, aeronautics, safety, regulations

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Semester Project , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-07-28 , Earliest start: 2025-08-05 , Latest end: 2026-07-31

Organization Automatic Control Laboratory

Hosts Brouillon Jean-Sébastien

Topics Law, Justice and Law Enforcement , Engineering and Technology

Games in Motion: Learning Equilibria in Metric Spaces

Imagine a strategic competition among multiple decision-makers in a broad scale. These can be a Democrat and a Republican competing for votes across a large population, or Pepsi and Cola battling for market shares in a vast region. What are the possible outcomes? How can one gain an edge compared to the opponent? These interactions can be characterized as equilibrium-seeking problems in metric probability spaces, featuring strategic decision-making under evolving distribution dynamics. We will bridge insights from game theory, dynamical systems, and optimal transport to shed light on solution concepts, algorithmic pipelines, and performance guarantees in such non-stationary environments.

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Game theory, dynamics, decision dependence, metric probability spaces.

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Semester Project , Master Thesis

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Published since: 2025-07-06 , Earliest start: 2025-07-01 , Latest end: 2026-06-30

Organization Automatic Control Laboratory

Hosts He Zhiyu

Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology

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ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland