Open Master's Thesis Positions
On this page you will find a selection of possible Master Thesis opportunities, some notified to us directly by the research groups of MEST Tutors and some listed on the SiROP database.
This list is not exhaustive, other Thesis projects might exist, please check the respective listings of Departments and research groups you are particularly interested in.
See also Internship opportunities.
Projects directly supplied by MEST Tutors
Master projects:
- external page Vertical Extensions: A Technological-Ecological Analysis through archetyping at the Chair of Architecture and Building Systems
- Download Assessing the innovation potential of electrochemical direct air capture (PDF, 122 KB) at the Energy and Technology Policy Group
Projects from the SiROP Database
ETH Zurich uses SiROP to publish and search scientific projects. Here is a selection of projects currently available which may be suitable for MEST students. For more information visit external page sirop.org.
De-bugging and Tuning of Self-Commissioning Controllers
This project focuses on developing an autonomous debugging and tuning system for self-commissioning controllers in HVAC applications. These controllers automatically learn system dynamics and configure PI gains, but their performance depends on precise hyperparameter settings and adaptive tuning to address issues like misconfiguration or changing conditions.
The goal is to create a program that analyzes controller behavior in real time, detects performance issues, and autonomously adjusts parameters for optimal operation. Key tasks include identifying critical hyperparameters, defining performance metrics, and designing a robust tuning algorithm. Numerical simulations will validate the approach across diverse scenarios, aiming for true plug-and-play functionality—enabling controllers to self-monitor and adapt like an expert engineer.
Ideal for students with a background in control systems or automation, the project offers hands-on experience in adaptive control, system identification, and smart building technologies. Proficiency in MATLAB/Simulink and basic machine learning knowledge are beneficial.
The project will be co-supervised with Belimo Automation AG.
Show details
Online Learning and Control for Control-Affine Systems
Classical control methods often rely on the assumptions on the uncertainties in the system. These can
be in the form of stochasticity assumptions, e.g. for the process/measurement noise, or in the form of a-
priori known/slowly varying reference signals, to name a few. In the modern world, control/reinforcement
learning algorithms are being increasingly deployed in complex applications, such as robotics, smart grid,
autonomous driving or building automation. During the real-life execution of algorithms on such complex
dynamical systems, the a-priori made assumptions may quickly become outdated necessitating the need for
online or adaptive changes of the method. This project considers a grey-box approach where we assume
to have a model of the dynamical system we are interested to control, but assume little about the additive
noise that the system encounters. Such an abstraction allows us not only to prove performance guarantees in
the form of regret, but also to provide safety and stability guarantees that are so crucial for cyber-physical
systems. The focus of the thesis will be on control-affine systems that appear often in practice. It will
explore how to pose the bilinear/control-affine quadratic control problem subject to additive non-stochastic
disturbances in the framework of online optimization and to derive state-of-the-art online controllers in this
setting. The results will be validated not only in simulation but also by rigorous theoretical guarantees in
the form of stability (BIBS) and dynamic regret.
Show details
Facilitating the Integration of Borehole Thermal Energy Storage in District Energy Systems: Improving the Fidelity of Design Tools
Borehole thermal energy storage emerges as a cost-effective solution to address the intermittency and seasonality challenges inherent in renewable energy sources. However, the integration of seasonal storage technologies is a challenging task. Today’s design and operation processes cannot cope with the higher complexity of renewable energy systems and required storage infrastructures, resulting in low deployment rates of technologies such as geothermal reservoirs. Recent publications studied methods to optimally design and integrate BTES systems, leveraging the use of automation during the operational stage [3]. However, limited effort has been placed in understanding the validity of the assumptions made during the design optimization stages and what is achieved during operation. The incorrect use of such simplification assumptions made at the design stage can lead to improper sizing of key components and, ultimately, to suboptimal operation of the entire energy system, regardless of the adopted automation techniques. This work aims at exploring the validity of different design- and control-oriented modelling tools and their applicability and performance in automation and optimization routines.
Show details
Online Calibration of a Digital Twin of a Residential Unit by Combining Physics-based and Machine Learning approaches
In this project, we build upon a Digital Twin with real-time capabilities from an existing Building Energy Model (BEM) of a residential unit located in the Nest building on the Empa campus and integrate it into a continuous calibration pipeline.
Show details
Safe Feedback Optimization for Power Grids via Switched Controllers
Feedback optimization is emerging as an important control method for modern power systems, thanks to its robustness and ability to steer the grid to an efficient operating point. Clearly, power systems are safety-critical infrastructures: failures can cause severe consequences, such as blackouts. In this project, we will design and evaluate novel feedback optimization schemes, based on switched systems, which can guarantee safety of the grid at all times.
Show details
From One-Click to Deep-Dive: Knowledge Levels for Vertical Extension Feasibility (Zurich)
This project maps which vertical-extension feasibility indicators require which level of building information—bridging detailed case studies with urban-scale (energy) modelling
Show details
VENTILATION DESIGN FOR BIPV FACADES
Using physical experimentation, explore ventilation cavity design for a BIPV facade.
Show details
GLOBAL ASSESSMENT OF IRRADIANCE THRESHOLDS FOR URBAN FACADE BIPV DEPLOYMENT
What urban surfaces are suitable for photovoltaics? In this project you will engage with this broad question through the narrow scope of solar and photovoltaic potential using life cycle impact assessment methods. The outcome will be a dynamic inventory of surface solar potentials to help guide more carbon-responsible PV deployment.
Show details
Thermal comfort in vertical extensions: Understanding the tradeoffs between light-weight construction and indoor heat resilience
This project investigates the trade-offs between material choices and indoor heat resilience in vertical extensions.
Show details
Transferable Flexibility Quantification through Clustering-Based Machine Learning Across Swiss Buildings
The growing integration of distributed renewable energy sources is essential for achieving low-carbon energy systems. However, the intermittent and unpredictable nature of renewables poses challenges to grid stability. Demand-side flexibility (DSF) — the ability of consumers to adjust or shift their electricity consumption — has emerged as a key enabler for maintaining grid balance. Buildings, as major energy consumers, offer significant DSF potential. Their flexibility can be harnessed through predictive energy management systems that optimize energy usage while maintaining occupant comfort. To quantify this flexibility,flexibility envelopes are commonly used, providing a compact representation of feasible load adjustments over time. Traditional optimization based methods for flexibility quantification, while accurate, are computationally intensive and difficult to scale across large building portfolios. Machine learning (ML) offers an efficient alternative by learning to predict flexibility from historical operational data. Yet, most current ML approaches are building-specific and require substantial data for each new case — limiting generalizability and transferability across diverse buildings. This project aims to overcome these limitations by developing a transferable ML-based flexibility quantification framework. The key idea is to cluster buildings across Switzerland based on key features influencing flexibility (e.g., building type, usage, HVAC system characteristics, and control strategies). For each cluster, a general or local ML agent will be trained to predict flexibility envelopes. When a new building is introduced, it will first be assigned to its most similar cluster, and the corresponding ML agent will be fine-tuned based on the available data for that specific building.
Show details
Hygrothermal characterization of hybrid earth-wood components
How can earth and wood work together to create naturally regulating building components? This thesis combines climatic-chamber experiments and 2D finite element modeling to assess the hygrothermal performance of a hybrid earth–wood slab and extract design insights for climate-responsive construction.
Show details
Resilience-Oriented Building Simulation under Extreme Weather Conditions
As climate extremes intensify, Typical Meteorological Year (TMY) weather files are increasingly insufficient to assess buildings, as they underrepresent heat extremes. This project develops and applies extreme weather datasets to assess how buildings perform during heatwave events, supporting improved risk assessments and adaptation strategies.
Show details
Optimal fertilizer application under imperfect ensemble weather forecasts using RL
This project combines algorithmic challenges in the design of machine learning based control algorithms with recent advances in precision agriculture. The student will implement, analyse and improve novel reinforcement learning approaches under partial state information. The algorithm will then be applied to achieve an optimal irrigation and fertilization scheme in precision agriculture under uncertain weather predictions. A simulator of the soil and plant dynamics under irrigation and fertilization is readily available. First numerical results using dynamic programming, treating weather as stochastic uncertainty, are highly promising, setting the stage for this research project.
Show details
Advanced Data Fusion and Signal Interpretation for In-Situ Monitoring and Quality Control in Laser Powder Bed Fusion
This thesis advances in-situ process monitoring in Laser Powder Bed Fusion (PBF-LB/M) by integrating Eddy Current Testing (ECT) and computer vision (CV) for defect detection. The study focuses on signal interpretation across simple to complex geometries, using multi-sensor data fusion to improve defect identification with real world data.
Show details
Optimization Controller for Gas Compressors Providing Grid Services
This thesis will be conducated in close collaboration with Everllence, a world-leading manufacturer of gas compressors. The aim is to develop an optimization-based controller for gas compressors that provide ancillary services to the power grid. First, the right controller architecture (data-driven, model-based, or hybrid) will be identified. Then, the main objective is to demonstrate the types of grid services that can be offered by compressor plant operators.
A cost-benefit assessment of the controller will quantify potential savings and identify key factors influencing the economic feasibility of using compressor applications for grid services.
Show details
Digital Twin for the ZCBS Lab’s Artificial Global Climate Chamber and Solar Simulator
The ZCBS climate chamber and solar simulator enable controlled testing of building envelope systems. This project develops a digital twin that replicates the chamber’s environmental conditions and testing setup, providing a reliable virtual environment to support test planning and validate experimental data.
Show details
AI for Grid Stability: Deep Learning the Small-Signal Dynamics of Power Converters
This project focuses on developing and validating a scalable machine learning framework to address the modeling challenges of small-signal stability assessment in grid-connected converters.
Show details
Distribution-Free State Estimation for Dynamical Systems via Conformal Prediction
Accurate state estimation in dynamical systems is traditionally achieved with model-based filters and smoothers such as the Kalman filter (KF), particle filters (PF), and Rauch-Tung-Striebel (RTS) smoothers. These methods offer optimal state estimates under specific probabilistic assumptions (e.g. linear Gaussian models) and provide transparency via interpretable models and theoretical guarantees (e.g. MMSE optimality of KF under Gaussian noise).
However, real systems often violate these assumptions as noise can be heavy-tailed or unknown and models mismatched leading to overconfident or inaccurate uncertainty estimates. Conformal prediction (CP) provides distribution-free uncertainty quantification with finite-sample coverage guarantees and minimal modeling assumptions. This thesis explores estimation algorithms that combines the transparency and structure of model-based filters/smoothers with CP's distribution-free coverage, studying theory and empirical behavior to obtain the best of both worlds for generic dynamical systems.
Show details
Interaction-Aware Control Design with Provable Guarantees in Realistic Robotic Scenarios
This project builds on the guarantees introduced in to design, analyze, and validate an interaction-aware control stack for robotic systems operating among responsive agents. The approach couples an internal trajectory-prediction module that is explicitly updated as policies change with a distribution-free safety layer based on conformal tools that retain finite-sample coverage in the presence of feedback-induced shift and adversarial perturbations. The resulting uncertainty sets are composed with certified planning and safety filters such as control barrier functions and reachability-based shielding. Evaluation encompasses software simulation and on-robot trials in realistic interactive scenarios and compares against confidence-aware and reachability/ORCA-style baselines.
Show details
Safe Manipulation in Complex Environments
Dynamical-system approaches for robotic control reshape velocity fields around obstacles for im-
pressive reactive avoidance. Yet, they can still encounter unwanted local minima and saddle points.
Research on Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) has estab-
lished conditions under which such equilibria arise and has provided controller-synthesis methods
to avoid them. By incorporating these safety and stability frameworks into DS approaches we hope
to introduce new solutions for real-time task execution in cluttered, real-world settings.
Show details
Uncertainty Modeling for Robotic Trajectory Optimization
Machine learning is increasing integrated into control systems as accurate and fast system models capable of representing nonlinearities. Gaussian Processes (GPs) are stochastic processes that can be used as a surrogate model for the system. They provide not only a prediction but a quantification of uncertainty, making them especially appealing for safety-critical systems. Despite the success of GPs in modeling system dynamics for data-efficient model-based reinforcement learning and safe Model Predictive Control (MPC), little work has studied the accuracy of GP derivatives, or how those errors propagate through optimization-based controllers. This project will contribute a theoretically grounded method for incorporating gradient uncertainty into control optimization, aiming to improve both safety and convergence time.
Show details
Verifiable Control Design with Data-Driven Spectral Submanifolds
Low-dimensional latent space representations of dynamical systems provide a powerful tool for scalable control design. Over the last years, learning-based approaches for constructing latent space representations have gained popularity, often utilizing variational autoencoders. While these approaches have shown great empirical success, they typically lack formal control guarantees as needed in safety-critical applications. In recent work, we presented a framework for designing controllers in such learned latent spaces that can provably guarantee stability and safety for the original system by exploiting approximate conjugacy between the latent and full dynamics. However, large approximate conjugacy can result in overly conservative and poorly performing control actions. On the other hand, spectral submanifolds were studied in as attracting invariant manifolds. Spectral submanifolds guarantee the existence of a low-dimensional latent space representation that, if restricted to the manifold, have the same behavior as the original system. Spectral submanifolds are constructed from the linear components of a dynamical system and have zero conjugacy error, even though their data-driven construction is approximate in nature. This thesis will investigate how to integrate the framework of spectral submanifolds into the latent space control design framework from \cite{lutkus2025latent} to achieve non-conservative control behavior while retaining stability and safety guarantees. The project combines insights from dynamical systems, control theory, and data-driven reduced order models.
Show details
Verifiable Latent Space Control Design Beyond Stability and Forward Invariance
Low-dimensional latent space representations of dynamical systems provide a powerful tool for scalable control design. Over the last years, data-driven approaches for constructing latent space representations have gained popularity and shown great empirical success. While these methods are promising, they typically lack formal control guarantees as needed in safety-critical applications. In recent work, we provided a theoretical framework for designing controllers in such learned latent spaces that can provably guarantee stability and safety for the original system by exploiting approximate conjugacy between the latent and full dynamics. Yet, these guarantees are largely limited to using notions of Lyapunov and barrier functions, ensuring stability and forward invariance only. Modern autonomous control systems, however, must often satisfy richer temporal or logical specifications that involve deadlines, sequencing, and reactive behavior. This thesis will investigate how to extend our latent space control design framework beyond stability and forward invariance, towards achieving verifiable temporal and logic-based system behavior. The project combines insights from representation learning, formal methods, and control theory, aiming to unify latent space learning with verifiable control design.
Show details
Is oversizing a mistake? Optimal heat pump capacities considering flexibility potential
This semester project explores the trade-offs between heat pump oversizing and flexibility potential through simulation. Ideally, an optimization algorithm considering the heat pump operation is developed and adapted to size the heat pump given flexibility constraints.
Show details
Behind Ride Fares: Dynamic Pricing for Mobility Systems
Imagine you are asked by Uber to set their base prices and cross-region prices for daily travel requests in Zürich. You would like to maximize the potential profit of Uber. Fortunately, you have rough ideas on the demand levels of Zürich and how they are affected by your chosen prices. Nonetheless, such random demands fluctuate every day, and they also drift with the conditions of other mobility service providers. How will you determine ideal Uber prices?
The above problem is an instance of stochastic optimization with decision dependence, where a decision maker interacts with a changing data distribution. We will develop an online algorithm based on daily samples and real-time interactions to navigate through the space of pricing vectors, thereby maximizing expected profits. We will leverage a dedicated agent-based simulator to demonstrate the effectiveness of the above strategy and compare it against benchmark schemes.
Show details
Self-supervised parallel manipulation learning via disagreement
Imitation Learning has proven able to replicate the fine dexterous manipulation movements by replicating human demonstrations. However, collecting such demonstrations is difficult to scale due to the need for a human expert in the data collection loop. A solution to this problem can be found in self-supervised exploration via disagreement.
Exploration by disagreement takes inspiration from classical active learning literature to devise a self-supervised procedure able to efficiently explore the model space.
Show details
Deep reinforcement learning for job shop scheduling
Production scheduling is a critical event in the production management of smart manufacturing systems. Many job shop scheduling (JSP) problems are known as NP-hard. Meanwhile, in the real production plants, not only is the objective of scheduling usually multiple (e.g., makespan, tardiness, machine utilization), but also the environment is mostly dynamic with unexpected machine breakdowns, order arrivals and cancellations, etc. Because the JSP can be modelled as a Markov Decision Process (MDP), using deep reinforcement learning techniques for scheduling has gained significant attention in this domain. In this project, a deep reinforcement learning environment for JSP will be developed and enhanced to tackle the aforementioned challenges.
Show details
Temporally Robust Controller Synthesis for Time-Critical Systems
The reliability of autonomous control systems depends on their robustness, i.e., their ability to meet their objectives under uncertainty. Emerging applications in autonomy are increasingly time-critical: failing to meet temporal requirements can severely compromise safety and performance, as in multi-robot disaster response, fleets of autonomous taxis, and automated airport ground control. Yet today’s technology still struggles with unpredictable delays and coordination failures, limiting their reliability in real-world settings. Existing research has focused on spatial robustness, ensuring that spatial objectives - such as collision avoidance of a robot - are met despite modeling errors and disturbances. However, time-critical systems also require temporal robustness, the ability to meet time-critical objectives - such as deadlines, sequencing, and periodic tasks - under uncertainty. A major challenge arises from variations in computation and actuation times, and particularly from complex timing uncertainties caused by human interaction, unpredictable sensing failures, and compute-intensive perception. This thesis will study novel ways to enable temporal robustness of autonomous control systems.
Show details
Feedback Control for Learning in Optimization and Game Theory
How can we design learning dynamics that are fast, robust, and provably correct when many decision-makers interact under shared constraints?
This project investigates this question by leveraging a novel control-theoretic framework for equality-constrained optimization to design and analyze dynamics that compute solutions to Generalized Nash Equilibrium problems in both static and time-varying settings.
Using contraction theory as a scalable framework for robust stability analysis, the goal is to derive verifiable conditions for global exponential convergence and robustness to disturbances, leading to algorithms with provable performance guarantees.
Show details
Grid-Connected Electrolyzer for Dynamic Ancillary Services Provision
This master thesis explores how hydrogen electrolyzers can contribute to power system stability through the provision of dynamic ancillary services while producing renewable hydrogen. In collaboration with H2 Energy, the project combines system modeling, control design, and experimental validation to investigate how grid-connected electrolyzers can deliver a range of grid services. The work aims to establish a comprehensive understanding of the technical feasibility, control strategies, and operational trade-offs associated with such multi-service operation without compromising electrolyzer health, efficiency, or hydrogen yield.
Show details
Fair Demand-Side Flexibility Allocation with Karma
The rapid integration of distributed renewable energy sources into electric power grids introduces the challenges of balancing intermittent power production and guaranteeing grid stability. Demand-side energy flexibility, which involves the end-user shifting or adjusting energy consumption in line with the supply and capacity of the electricity grid, is receiving increasing attention as an important approach towards tackling these challenges. Buildings are significant energy consumers and hence present a promising source of flexibility. Leveraging the thermal energy storage capabilities of buildings allows to intentionally modify their energy consumption to support grid needs without compromising occupants’ thermal comfort. Previous work has focused on quantifying energy flexibility envelopes or designing temperature control strategies which are important to meet flexibility objectives. However, in practice, Distribution System Operators (DSOs) often prefer to provide flexibility targets to groups of buildings rather than to individual units. This leads to the question of how to fairly and efficiently distribute flexibility targets to individual households within a designated group. This project aims to develop a karma mechanism for this flexibility allocation problem. Karma economies are a promising recently developed non-monetary solution to the allocation of shared resources. These economies leverage the fact that shared resources, such as flexibility requests, do not occur once but are repeated frequently. It is thus envisioned that karma will enable consumers to express when they have high urgency to avoid shifting their consumption, meanwhile guaranteeing that everyone contributes fairly to providing flexibility over time.
Show details
Online Feedback Optimization for Real Time Power Grid Control
Online Feedback Optimization (OFO) allows to drive a dynamical system to a steady state that satisfies given optimality criteria, such as efficiency and satisfactions of constraints.
In the past years, we had great success in applying OFO to the real-time control of power grids, both for energy distribution and transmission. With RTE France we simulated how to solve grid congestion when there is an excess of wind generation. We deployed an OFO algorithm on the Swiss grid to control the voltage and the reactive power of a distribution grid. And we won the Watt D'Or award by the Swiss Federal Office of Energy!
Show details
Games in Motion: Learning Equilibria in Metric Spaces
Imagine a strategic competition among multiple parties for the attention of the general public with complex interactions. 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, complex networks, and optimal transport to shed light on solution concepts, algorithmic pipelines, and performance guarantees in such non-stationary environments.
Show details
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.
Show details
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.
Show details
Review of policy frameworks supporting Positive Energy Districts (PEDs) in Switzerland
Positive Energy Districts (PEDs) are central to Europe’s mission for climate-neutral cities. However, their realization depends strongly on enabling policy frameworks at national and local levels. This Master’s thesis aims to map and analyze Swiss policies that support, directly or indirectly, the emergence of PEDs, and to position them within a broader European context. The student will identify existing gaps, overlaps, and opportunities across energy, building, urban planning, and mobility sectors, contributing to the ongoing European research project Pol4PED (Policies for Positive Energy Districts).
Show details
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.
Show details
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.
Show details
Optimal Excitation for Grid Impedance Estimation
This project aims to develop optimal excitation schemes for impedance estimation of grid/grid-connected converters.
Show details
Geometry -aware Patch Fitting for Developable Tensile Structures
We introduce an interactive computational-geometry tool that matches near-developable surface patches to available fabric pieces. The patch-to-fabric assignment is posed as a constrained optimization and 2D nesting problem—respecting size, grain orientation, curvature tolerance, and seam placement—while minimizing waste and enabling aesthetic layout control, enabling reuse of materials such as sailcloth in tensile structures.
Show details
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.
Show details
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.
Show details
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.
Show details
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.
Show details
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.
Show details