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
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.
Keywords
Modeling, nonlinear control, system identification, learning-based control, state estimation, fluid dynamics
Labels
Semester Project
Description
Goal
Contact Details
More information
Published since: 2025-04-14 , 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
Iterative Learning Control for Additive Manufacturing Processes
Additive manufacturing, commonly known as 3D printing, has seen widespread adoption in many engineering fields, including biomedical, aerospace, and automotive applications. Its ability to manufacture complex designs accurately and quickly offers significant advantages in rapid prototyping, customization, and design flexibility for mechanical assembly. As an additive manufacturing technique, fused deposition modeling (FDM) is governed by two key dynamics: motion and extrusion. In FDM, a triple axis system controls the movement of the extrusion head, which melts and deposits plastic filament onto the printing bed. However, these dynamics are inherently coupled, whereby the extrusion width is directly affected by the motion of the system. Given that the printing motion is highly repetitive, it is advantageous to leverage this behavior when designing a control system.
Keywords
Motion control, adaptive control, learning control, robotics
Labels
Semester Project , Master Thesis
PLEASE LOG IN TO SEE DESCRIPTION
More information
Published since: 2025-03-27 , Earliest start: 2025-03-31
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Hoteit Rawan , Zuliani Riccardo , Balta Efe
Topics Engineering and Technology
Feedback Optimization for Freeway Ramp Metering
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.
Keywords
Feedback optimization, monotone systems, freeway ramp metering, timescale separation
Labels
Master Thesis
Description
Goal
Contact Details
More information
Published since: 2025-03-19 , Earliest start: 2025-01-15 , Latest end: 2025-12-15
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa
Organization Automatic Control Laboratory
Hosts Chandrasekaran Sanjay , Bianchi Mattia
Topics Engineering and Technology
Data-driven Safe Control Design: A Certificate Function Approach
Safety 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.
Keywords
Data-driven control, model-free control, optimal control, safety-critical systems
Labels
Semester Project , Master Thesis
Description
Goal
Contact Details
More information
Published since: 2025-03-03 , Earliest start: 2025-03-03
Organization Automatic Control Laboratory
Hosts Wang Han , Moffat Keith , Fochesato Marta
Topics Engineering and Technology
Data-Driven Power System Stabilization with Dissipativity-Informed Neural Networks
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.
Keywords
Deep learning, data-driven control, dissipativity, power systems
Labels
Semester Project , Master Thesis
Description
Goal
Contact Details
More information
Published since: 2025-02-13 , Earliest start: 2025-02-17
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Wang Han , Moffat Keith
Topics Engineering and Technology
Designing High-Performance MPC Controllers Under Environmental Changes Using Meta-Learning
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.
Keywords
Meta-Learning, Model Predictive Control, Learning-based Control
Labels
Semester Project
Description
Goal
Contact Details
More information
Published since: 2025-02-12 , Earliest start: 2025-02-16
Organization Automatic Control Laboratory
Hosts Schmid Niklas
Topics Engineering and Technology
Identifying influencers in social networks
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.
Keywords
Control theory, online feedback optimization, social networks, opinion dynamics, Kalman filtering, recommender systems
Labels
Semester Project , Master Thesis
Description
Goal
Contact Details
More information
Published since: 2024-11-29 , Earliest start: 2025-02-17 , Latest end: 2025-12-15
Applications limited to EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich
Organization Automatic Control Laboratory
Hosts Chandrasekaran Sanjay
Topics Mathematical Sciences , Engineering and Technology
Contact
ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland