 Automatic Control LaboratoryOpen OpportunitiesModel 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
| 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
| In this project, we study Iterative Learning Control (ILC), which is a repetitive controller that uses feedback to improve performance over iterations. We formulate the ILC problem as an optimization problem with the physical system as a constraint. Specifically, we seek to apply ILC algorithms to practical applications in robotics and manufacturing.
Some potential applications include 3D printing of polymers or metals; Learning-based drone flight path optimization; Closed-loop control of planar and non-planar extrusion-based additive manufacturing; Robot-based machining processes; and Precision motion control.
The project will extend existing methods and specialize them for the application domain to provide a full demonstration of the potential of the controllers in various realistic scenarios. The specific application will be decided on the student's background and interests. The output of the project is the development and demonstration of learning controllers for various tasks. This project is part of the Research Explanation and Application Lab (REAL) initiative; a special focus will be put on research explanation and presentation skills; students working on the project will receive dedicated training. - Electrical and Electronic Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- Applications (IfA), 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
| This project aims to use two converter emulators available in the Automatic Control Laboratory of ETHz to experimentally validate a new impedance estimation approach. The main goals are to replicate realistic converter/grid conditions, assess the accuracy and robustness of the estimation method, and to explore its limitations and performance boundaries. - Engineering and Technology
- Master Thesis
| This project aims to develop optimal excitation schemes for impedance estimation of grid/grid-connected converters. - Engineering and Technology
- Master Thesis
| Various strategic interactions involve hierarchical decision-making processes, where one entity leads and others react accordingly. Stackelberg games provide a mathematical framework to model such scenarios, capturing the dynamics between a leader and multiple followers. However, in many real-world applications of such structures, we often only observe the response of the followers but we are unsure about the optimization problem that the followers are optimizing. This research
question, also known as inverse game theory, poses significant challenges, further complicated by noisy observations, bounded rationality, and many more. This project aims to develop methodologies for inferring the utility functions of followers in such scenarios by leveraging observed actions and partial knowledge of their parameters, working on Swissgrid energy market data provided by the MAESTRO project.
- Applied Economics, Applied Statistics, Numerical Analysis, Optimisation, Simulation and Modelling
- Master Thesis
| Modern buildings' HVAC (Heating, Ventilation, and Air Conditioning) systems incorporate a complex network of sensors, control units, and actuators working in coordination across multiple levels to ensure optimal operation. Key building control tasks include regulating air quality, temperature, and ventilation. Achieving efficient building control is critical for occupant comfort and meeting energy efficiency and sustainability targets. Due to the substantial energy consumption associated with buildings, enhancing operational efficiency by leveraging data analytics for control has a high potential for energy savings and sustainability gains. Effective control strategies can, in many practical cases, significantly reduce CO2 emissions from buildings. - Control Engineering, Electrical Engineering, Interdisciplinary Engineering, Mechanical Engineering, Systems Theory and Control
- Master Thesis
| This project will investigate how the
assumption of rationality affects leader-follower dynamics in Stackelberg games, particularly focusing on the potential loss of the leader’s first-mover advantage when followers act irrationally. We will examine scenarios where followers employ non-credible threats, take into account empirical evidence of irrational behavior and frame communication noise as a form of bounded rationality among followers. The aim of the project is to show that followers can strategically exploit their ”irrationality” to diminish the leader’s influence and to propose new insights into strategic interactions where rationality cannot be assumed, with implications for policy-making and other leader-follower contexts. - Mathematical Economics, Operations Research, Optimisation
- Master Thesis
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