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

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.

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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

Applications of Iterative Learning Control

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.

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Repetitive control; Learning-based control; Iterative learning control; Trajectory optimization; Industrial applications; Manufacturing automation; Robotics

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Semester Project , Master Thesis , Applications (IfA)

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Published since: 2024-11-19 , Earliest start: 2025-01-20

Applications limited to ETH Zurich

Organization Automatic Control Laboratory

Hosts 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.

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Feedback optimization, monotone systems, freeway ramp metering, timescale separation

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Semester Project , Master Thesis

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Published since: 2024-11-18 , 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

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

Description

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

Enhancing Model Predictive Control with Reinforcement Learning

Model Predictive Control (MPC) is extensively utilized in industry and academia thanks to its ease of use and flexibility. However, MPC is an inherently suboptimal control technique, and could perform poorly in presence of external disturbances or unmodelled dynamics. Many solutions that aim at robustifying MPC exist, but they are generally overly conservative and difficult to implement. This project seeks to obtain robust MPC schemes that achieve high performance in challenging control tasks by using tools from reinforcement learning through the application of gradient-based optimization schemes.

Keywords

Model predictive control, Reinforcement Learning

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Master Thesis

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Model predictive control (MPC) is ubiquitus in industry and academia. The core idea of this control technique is to utilize a model of the process dynamics to construct a prediction of how the system will evolve within a certain prediction horizon, starting from a given initial state. Next, the MPC dynamically determines the optimal control actions within the horizon by solving an optimization problem with the goal of minimizing some cost function (for example steering the state of the system to the origin or tracking a reference). When deployed on a real system, the optimization is repeated at each time-step, and the control sequence is applied only for the first portion of the prediction horizon. As time progresses, the optimization process is repeated, and the horizon "recedes" forward in time. This constant re-optimization ensures that the controller actively responds to variations of the environment, thus enabling feedback. The prediction that the MPC makes at every timestep is referred to as "open-loop" control, since it doesn't take into account future measurements beyond the current time. The main source of suboptimality in MPC is the discrepancy between the open-loop prediction and the true trajectory (closed-loop) obtained with the receding horizon paradigm. This is because the objective function in the MPC problem involves the open-loop, rather than the closed-loop. One way to reduce the suboptimality is to increase the length of the prediction horizon. In this way the MPC is able to capture the system's evolution over a longer period, thereby making choices that better discount between short and long term benefits. However, a longer prediction horizon requires more computational resources and this may not be possible in applications where the MPC is expected to run at fast rates. Another possibility to improve the closed-loop performance of MPC is to modify the cost function of the MPC problem in such a way that the control action is chosen optimally for the closed-loop. We can obtain the optimal cost function by solving a policy optimization problem. These problems, generally addressed in the field of reinforcement learning, involve adjusting the parameters of the policy (in this case, the MPC controller) to improve its performance. Reinforcement learning algorithms can be implemented using gradient-based methods. These methods involve computing the gradient of some performance measure with respect to the MPC parameters and then updating the parameters in the direction that improves performance. To obtain the gradient of the entire closed-loop trajectory with respect to the design parameters, we can utilize the approach proposed in [1], which utilizes a backpropagation scheme. The same gradient-based techniques can also be used to ensure that the MPC scheme is robust against unmodeled dynamics and / or stochastic noise [2] (see pdf for a numerical example).

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Published since: 2024-11-12 , Earliest start: 2024-11-30

Organization Automatic Control Laboratory

Hosts Zuliani Riccardo , Balta Efe

Topics Mathematical Sciences , Information, Computing and Communication Sciences

Conducting an Orchestra: Learning in Stackelberg Games with Maestro

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.

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Game Theory, Learning, Data Analysis, Energy Market

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Published since: 2024-11-11 , Earliest start: 2024-11-17 , Latest end: 2025-07-31

Organization Automatic Control Laboratory

Hosts Shilov Ilia , Bolognani Saverio

Topics Mathematical Sciences , Information, Computing and Communication Sciences , Economics

Data-driven Control in Building Energy Systems

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.

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Data-Driven Control, Adaptive Control, DeePC, Reinforcement Learning, Buildings, HVAC

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Master Thesis

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Published since: 2024-11-11 , Earliest start: 2024-11-10

Organization Automatic Control Laboratory

Hosts Balta Efe , Spoek Ben

Topics Information, Computing and Communication Sciences , Engineering and Technology

Becoming Ungovernable: The Erosion of Leadership Advantage with Strategically Irrational Followers

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.

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Game Theory, Mechanism Design, Bounded Rationality, Learning in Games

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Published since: 2024-11-11 , Earliest start: 2024-11-17 , Latest end: 2025-07-31

Organization Automatic Control Laboratory

Hosts Shilov Ilia , Bolognani Saverio

Topics Mathematical Sciences , Economics

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ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland