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
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
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. AWE systems are flying objects, which are strictly regulated. Although our breakthrough can allow for lighter and safer wings, a close contact with authorities is required to avoid unnecessary risks later on.
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
AI-based optimization of wet-milling processes
Wet-milling is a critical process in various industrial sectors, where the grinding efficiency significantly influences both economic and environmental performance. This thesis project aims to optimize wet-milling operations by leveraging artificial intelligence, specifically Bayesian optimization and neural networks, to determine optimal process parameters. The work will begin with a comprehensive system identification phase, modeling the nonlinear dynamics of the milling process while accounting for varying feed materials and bead characteristics. Subsequently, a data-driven optimization pipeline will be developed and validated to enhance operational efficiency. This interdisciplinary project combines control theory, machine learning, and process engineering, with potential contributions to academic publications and real-world industrial applications.
Keywords
Bayesian optimization, state-estimation, system-identification, learning-based control
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Semester Project , Master Thesis
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Published since: 2025-07-14 , Earliest start: 2025-09-01 , Latest end: 2026-02-01
Organization Automatic Control Laboratory
Hosts Zakwan Muhammad , Balta Efe
Topics Engineering and Technology
Advanced Volume Control for Pipetting
Improving volume control precision and robustness in automated pipetting remains a challenge, often limited by traditional indirect methods. This project explores direct volume control by leveraging internal air pressure measurements and the ideal gas law. Key obstacles include friction, pressure oscillations, varying liquid viscosities, evaporation, and liquid retention. Collaborating with Hamilton Robotics, the goal is to develop a robust control architecture for their precision pipette (MagPip) suitable for diverse liquids. The approach involves mathematical modeling based on sensor data, designing robust control strategies to handle nonlinearities and disturbances, and validating through simulation and real-world experiments.
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Modeling, nonlinear control, system identification, learning-based control, state estimation, fluid dynamics
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Semester Project
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Published since: 2025-07-11 , Earliest start: 2025-05-01 , Latest end: 2025-09-01
Organization Automatic Control Laboratory
Hosts Zakwan Muhammad
Topics Mathematical Sciences , Information, Computing and Communication Sciences , 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.
Keywords
Game theory, 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
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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Published since: 2025-06-08 , Earliest start: 2025-07-01
Organization Automatic Control Laboratory
Hosts Zuliani Riccardo
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: 2025-06-06 , 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
Contact
ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland