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

Iterative Learning Motion and Extrusion 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

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Semester Project , Master Thesis

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Published since: 2025-03-27 , Earliest start: 2025-03-31

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts Hoteit Rawan , Zuliani Riccardo

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

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Master Thesis

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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

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Semester Project , Master Thesis

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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

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Semester Project , Master Thesis

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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

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Semester Project

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Published since: 2025-02-12 , Earliest start: 2025-02-16

Organization Automatic Control Laboratory

Hosts Schmid Niklas

Topics Engineering and Technology

System theory of iterative methods

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.

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Master Thesis

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Published since: 2025-01-17

Organization Automatic Control Laboratory

Hosts Eising Jaap

Topics Mathematical Sciences , Information, Computing and Communication Sciences , 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

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Semester Project , Master Thesis

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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

Experimental Validation of an Impedance Identification Method for Three-Phase Power Systems

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.

Keywords

Impedance Estimation; Grid-connected converters; Power electronics; System Identification

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Master Thesis

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The integration of renewable energy sources and distributed generation in modern power systems has increased significantly. This resulted in an increased use of power-electronics-based converters, changing the dynamical behaviour of the grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. It is also used for optimizing the closed-loop performance via, e.g., adaptive control design. However, due to the grid's dynamic and nonlinear nature, impedance estimation becomes particularly challenging, in particular when conducted in real-time under closed-loop operation (see e.g., [2]). A new non-parametric identification method has shown potential for accurate and rapid grid impedance identification. While initial simulations have demonstrated the potential of the method, experimental validation using hardware is crucial to evaluate the method's performance under real-world conditions. This project aims to use two converter emulators ([3]) available in the Automatic Control Laboratory to experimentally validate the new approach. The main goals are to replicate realistic converter/grid conditions, assess the accuracy and robustness of the method, and to explore the limitations and performance boundaries of the method. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] https://imperix.com/products/power-electronics-test-bench/ **Prerequisites** We are looking for a motivated student with a background in power systems/power electronics, and a previous exposure to system identification or signal processing. Knowledge of grid-connected converters and impedance estimation methods would be highly beneficial. Familiarity with experimental configurations and lab instrumentation is desirable. Competitive candidates will have prior research or work experience that demonstrate the ability to learn and problem-solve independently.

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Published since: 2024-11-15 , Earliest start: 2024-11-17

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts Abdalmoaty Mohamed , He Xiuqiang

Topics Engineering and Technology

Optimal Excitation for Grid Impedance Estimation

This project aims to develop optimal excitation schemes for impedance estimation of grid/grid-connected converters.

Keywords

Impedance estimation; grid-connected converters; optimal excitation; experiment design; system identification

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Master Thesis

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The use of renewable energy sources and distributed generation, such as wind turbines and solar power plants, in modern power systems has been increasing significantly. This resulted in a heightened use of power-electronics-based converters, changing the dynamical behaviour of the power grid and raising the need for accurate grid impedance estimation methods. Knowledge of grid impedance at the point of common coupling is essential for online stability assessment of grid-connected converters [1]. However, due to the grid's dynamic and nonlinear nature, impedance estimation is particularly challenging (see e.g., [2]). The grid-connected converter's controller must maintain stable operation while simultaneously allowing the injection of small excitation signals necessary for grid impedance estimation. This creates a fundamental trade-off: while a good controller and converter-grid interface filter improve system stability and disturbance rejection, they can also suppress the grid's response to the excitation, reducing the estimation accuracy. On the other hand, injecting larger excitation signals could improve the quality of estimation but risks triggering the converter protection system and/or exciting nonlinear grid behaviors. Moreover, it is not clear what the best class of excitation signals is nor the best injection point within the converter controller. This project aims to address this trade-off and questions by developing optimal excitation schemes for grid impedance estimation using grid-connected converters. A main goal is to obtain a good signal-to-noise ratio at the frequencies of interest while respecting experimental time and signal magnitude constraints. A class of excitation signals of potential interest is random phase multisines [3]. Recent related results in the literature are [4,5] which are based on modelling the noise and the use of multiple sinusoidal injections. References: [1] J. Sun, "Impedance-Based Stability Criterion for Grid-Connected Inverters," in IEEE Transactions on Power Electronics, vol. 26, no. 11, Nov. 2011 [2] V. Haberle, et.al., "MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches," 2023 62nd IEEE CDC [3] Godfrey, Keith, ed. Perturbation signals for system identification. Prentice Hall International, 1993. [4] Y. Zhu, et.al, "Injection Amplitude Guidance for Impedance Measurement in Power Systems," in IEEE Transactions on Power Electronics, vol. 38, no. 6, June 2023 [5] H. Li, et.al., "A Noise Interference Suppression Method With Optimized Frequency Adjustment for Impedance Measurement," in IEEE Transactions on Industrial Electronics, vol. 71, no. 12, Dec. 2024 **Prerequisits** We are looking for a motivated student with excellent written and verbal communication skills, and the ability to learn and problem-solve independently. Competitive candidate will have a background in systems & control, signal processing, and power systems/electronics. A prior experience or coursework in system identification and/or grid-connected converters would be highly beneficial. Proficiency in MATLAB/Simulink or a similar simulation environment is required for implementing and testing the methods.

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Published since: 2024-11-15 , Earliest start: 2024-11-17

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts He Xiuqiang , Abdalmoaty Mohamed

Topics Engineering and Technology

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ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland