RectorateOpen OpportunitiesSafety is a fundamental requirement for critical systems such as power converter protection, robotics, and autonomous vehicles. Ensuring long-term safety in these systems requires both characterizing safe behaviour and designing feedback controllers that enforce safety constraints. Control Barrier Functions (CBFs) have recently emerged as a powerful tool for addressing these challenges by defining safe regions in the state space and formulating control strategies that maintain safety. When the dynamical system is precisely modeled, a CBF can be designed by solving a convex optimization problem, where the state-space model is incorporated into the constraints.
However, designing valid CBFs remains difficult when system models are uncertain or time-varying. More importantly, CBFs and control laws derived from inaccurate models may lead to unsafe behaviour in real-world systems. To overcome these difficulties, this project aims to develop a data-driven approach for constructing CBFs without relying on explicit system models. Instead, we will leverage behavioural systems theory to replace model information in the design program by persistently exciting data. The proposed method will be applied to output current protection in power converters or robotics collision avoidance. - Engineering and Technology
- Master Thesis, Semester Project
| Modern power systems exhibit significant complexity, making their analysis and control particularly challenging, especially when precise system models are unavailable. Traditional model-based control strategies often fail to scale with increasing system complexity, while recent advances in nonlinear, learning based control offer promising alternatives. However, many of these methods lack formal stability guarantees, which are crucial for safety-critical applications such as power system frequency control. This project aims to bridge this gap by developing a deep learning framework for analyzing the dissipativity properties of power systems and designing stabilizing controllers with formal guarantees. - Engineering and Technology
- Master Thesis, Semester Project
| Model predictive control (MPC) is a widely used control technique that optimizes control inputs while fulfilling process constraints. Although automated tuning methods have been developed for task-specific MPC, they struggle when tasks change over time, requiring costly re-tuning. This project aims to reduce the computational burden of re-tuning by leveraging meta-learning, enabling efficient adaptation of controllers to different environments with minimal data. - Electrical Engineering
- Semester Project
| This research focuses on optimizing district thermal network design and phasing strategies by incorporating existing infrastructure and studying interplays with urban design metrics. While many existing district thermal network models often assume the absence of prior infrastructure, this study introduces methodologies to account for existing pipes, plants, and pumps, enabling a more realistic scenario for network planning. Additionally, the research explores phased implementation strategies to maximize return on investment under budget constraints, providing a pathway for incremental network construction and operation.
A key aspect of the study is the feedback loop between urban design and thermal network engineering, which quantifies how urban parameters such as building density, land use types, and development phasing affect network performance, and vice versa. The proposed methodologies are applied to case studies in Zurich, Singapore, and Shanghai, representing diverse climatic and urban contexts.
The outcomes include a framework for integrating existing infrastructure, strategies for phased implementation, and insights into the dynamic interplay between urban design and district energy systems. If time permits, the research will also produce a computational prototype for integration into the City Energy Analyst (CEA).
- Architecture, Urban Environment and Building
- Master in Integrated Building Systems (ETHZ), Master Thesis
| Large-scale polymer 3D printing offers unique geometric freedom and performance integration, enabling the creation of lightweight, sustainable, and functional facade systems. Within the existing research efforts of NCCR DFAB, this project focuses specifically on mechanical testing to compare different polymer materials for use in 3D-printed facade systems. The research evaluates mechanical properties such as bending, impact, and tensile strength to determine the most suitable materials for lightweight facade components. Comparisons are made based on how the materials meet mechanical performance requirements for functional facades. - Building
- ETH Zurich (ETHZ), Master in Integrated Building Systems (ETHZ), Semester Project
| Digital and robotic fabrication techniques are increasingly being explored to create building components with embedded functionalities, offering unparalleled opportunities for customization.
As the adoption of robotic 3D printing grows, it becomes crucial to evaluate the environmental impacts of these processes, particularly their energy consumption and associated emissions. Understanding these impacts is essential to assess the sustainability of robotic 3D printing processes.
This project, enabled by real-world data provided by Saeki Robotics, aims to develop a predictive model to assess and forecast energy consumption and emissions in robotic 3D printing. - Architecture, Urban Environment and Building
- ETH Zurich (ETHZ), Master in Integrated Building Systems (ETHZ), Semester Project
| Rapid urbanization has intensified the Urban Heat Island (UHI) effect in many cities worldwide, leading to higher ambient temperatures and reduced thermal comfort. Building surfaces play a pivotal role in this process, as their materials and configurations affect how heat is absorbed and re-emitted into the surrounding environment. To better understand and mitigate these effects, this master’s thesis will investigate the thermal behavior of selected façade wall assemblies under controlled “sunlight” conditions using the Solar Simulator at the Zero Carbon Building Systems (ZCBS) Lab. - Architecture
- Master in Integrated Building Systems (ETHZ)
| Modern control methods often rely on explicit online computation. In order to understand such closed loops between numerical methods and dynamical systems, this project
approaches the algorithm as a dynamical system itself. In doing so, the usual
language of convergence of algorithms can be viewed as a special case of stability
theory. - Control Engineering, Numerical Analysis, Systems Theory and Control, Systems Theory and Control
- Master Thesis
| The objective of this project is the design and analysis of recommender systems as optimization algorithms representing a robust feedback controller. We aim to design recommender system algorithms that identify influential users using observable data from users (for example: clicks/ time spent on a page/ likes etc.) in a social network and provide recommendations accordingly. - Engineering and Technology, Mathematical Sciences
- Master Thesis, Semester Project
| Online Feedback optimization (OFO) is a beautiful control method to drive a dynamical system to an
optimal steady-state. By directly interconnecting optimization algorithms with real-time system measurements, OFO guarantees robustness and efficient operation, yet without requiring exact knowledge
of the system model. The goal of this project is to develop faster OFO schemes for congestion control
on freeways, in particular by leveraging the monotonicity properties of traffic networks. - Electrical Engineering
- Master Thesis, Semester Project
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