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
Online identification of time-varying systems using data-driven models - a case study
We consider a novel model for dynamical systems that gave rise to new and highly performant data-driven control methods recently. Even though these methods are typically limited to linear time-invariant systems, recent work has proposed adapting the model online using tools for subspace tracking. This project aims to demonstrate the method’s effectiveness through case studies, highlighting its potential for superior performance in practical applications.
Keywords
system identification, data-driven control, time-varying systems
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Semester Project
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Published since: 2024-12-18 , Earliest start: 2025-01-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Automatic Control Laboratory
Hosts Sasfi Andras
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
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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
Crypto-governance with karma
The revolutionary appeal of cryptocurrencies and the underlying distributed ledgers is that no one owns them. They are highly democratic systems (at least in principle): the community sets the rules of the ledger and maintains it. This has the unique feature of being highly dynamic and adaptable to the latest greatest in technology and societal needs. But to fully deliver on their appeal, distributed ledgers must employ a fair and efficient mechanism for self-governance. Should a ledger change its protocol, e.g., from proof-of-work to proof-of-stake? How should a newly identified bug be resolved? Many distributed ledgers have adopted voting-like mechanisms for this purpose, but crucially, voting rights are associated with the amount of tokens owned, and as a direct consequence, with the wealth of the users, contradicting the most basic principles of democracy. However, unlike in classical political decisions, crypto-governance decisions are highly dynamic and frequent - they almost occur in real-time. This makes them especially suited for a karma economy, which has been recently demonstrated to achieve highly fair and efficient outcomes in repetitive settings in a completely non-monetary manner.
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Semester Project , Master Thesis
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Published since: 2024-11-19 , Earliest start: 2025-01-06
Organization Automatic Control Laboratory
Hosts Elokda Ezzat
Topics Information, Computing and Communication Sciences
Reinforcement Learning Controller for Assembly in Motion with Franka Emika Robot
In smart manufacturing, robot systems are crucial for performing various tasks in complex environ- ments on the fly. In this project, we will develop a learning controller that performs challenging inser- tion tasks by interacting with the environment.
Keywords
Reinforcement Learning, Artificial Intelligence, Robot Control, Smart Manufacturing
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Semester Project , ETH Zurich (ETHZ)
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Published since: 2024-11-19 , Earliest start: 2025-01-06 , Latest end: 2025-06-30
Organization Automatic Control Laboratory
Hosts Nobar Mahdi
Topics Information, Computing and Communication Sciences , Engineering and Technology
Kinematics Robot Model Calibration
It is important to identify a reliable and accurate model of a robot for many applications, such as a control design. This project attempts to improve the accuracy of a physical model of a collaborative robot arm.
Keywords
Kinematics model, Robot, Camera, Sensor, Hardware
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Semester Project , Bachelor Thesis , ETH Zurich (ETHZ)
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Published since: 2024-11-19 , Earliest start: 2025-01-06 , Latest end: 2025-06-30
Organization Automatic Control Laboratory
Hosts Nobar Mahdi
Topics Information, Computing and Communication 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.
Keywords
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
Optimal Crop Fertilization Control Strategies and Verification
This project deals with the design and analysis of fertilization control strategies. The goal is to minimize over-fertilization while ensuring sufficient nutrification of the crops. Therefore, it is required to study literature on dynamical models of nitrogen in soil, extract a suitable model and implement it in a simulation. Then, design a suitable, formally verifyable control algorithm and analyse the potential of optimal fertilization strategies in agriculture. The control tools may range from dynamic programming (with a-priori guarantees) to reinforcement learning (with statistical a-posteriori guarantees) and beyond.
Keywords
Control Theory, Agriculture, Fertilization, Formal Methods, Safety, Stochastic Systems, Reinforcement Learning
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Master Thesis
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Published since: 2024-11-18
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Schmid Niklas
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Karma games for proportional resource allocations in population with variable clusters
Karma games belong to the class of Dynamic Population Games (DPG). They are formulated as repeated auction-like games for a population of self-interested agents and ensure fair and efficient resource allocation in such a population. Motivated by its application for priority distribution among Connected and Automated Vehicles (CAVs), we are interested in designing a karma game for proportional resource allocations in populations with variable clusters. The research question is described with an example of CAV traffic. Assume CAVs are assigned into clusters based on safety criteria and jointly take actions to avoid collisions. Every time a new collision is detected, a new cluster is formed, lasting until the threat is solved. The number of CAVs within a cluster and the cluster duration are variable. CAVs compete to win priority values inside clusters. How can we design a karma game to distribute priority fairly and efficiently among all the CAVs? The applications of such a game are not limited to CAVs; they can be further extended for other applications of proportional resource allocations, such as shared servers.
Keywords
Karma games, Dynamic population games, Proportional resource allocations,
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Semester Project , Master Thesis
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Published since: 2024-11-18
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Chavoshi Kimia , Elokda Ezzat
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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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
Reinforcement Learning Control with Probabilistic Safety
When controlling a system we typically aim to make the system carry out specific tasks, like remaining in a set of states, or reaching a set of states, or both. Recent advances allow to formulate controllers using dynamic programming that trade off such specifications optimally against costs, such as energy consumption. However, these methods rely on full model knowledge; it is the aim of this project to explore learning-based algorithms towards achieving these objectives. The approach will be validated on the Ball-on-a-Plate system, which is a mechanically actuated plate with a ball on it.
Keywords
Machine Learning, Reinforcement Learning, Control Theory, Safety, Stochastic Systems
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Master Thesis
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Published since: 2024-11-18
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Schmid Niklas
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Model Predictive Tracking Control of Franka Emika Panda Robot in Simulation
This project realizes a model-based optimal controller for a complex robot arm in simulation.
Keywords
MPC, Robot, control, manufacturing, data-driven control. Machine learning,
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2024-11-18 , Earliest start: 2025-01-06 , Latest end: 2025-06-30
Organization Automatic Control Laboratory
Hosts Nobar Mahdi
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Divergence for convergence: Stability guarantees via Bregman divergence
Computational tools for finding Lyapunov functions are the core of many control design and verification tasks, such as choosing terminal ingredients in MPC, or formally guaranteeing stability for complex nonlinear systems. We have recently proposed a new method for finding Lyapunov functions, based on Bregman divergences. The goal of this project is to test, validate and further develop this method, via numerical experiments, and application to toy examples as well as to challenging problems in power systems.
Keywords
Lyapunov functions, Bregman divergence, computational methods, SOS optimization, neural networks
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Semester Project , Master Thesis
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Published since: 2024-11-16 , Earliest start: 2025-01-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa
Organization Automatic Control Laboratory
Hosts Bianchi Mattia
Topics Information, Computing and Communication Sciences , Engineering and Technology
Hopping (and Hoping) for Stability: Data-Driven Control of Microgrids with Markov Jumps
Markov Jump Linear Systems (MJLS) are dynamical systems that switch randomly among different dynamics, according to a Markov chain. One example is provided by energy microgrids, which operate in islanded or grid-tied modes, depending on some stochastic events. The goal of this project is to develop data-driven controllers for this type of systems, that can guarantee stability despite the switching between different operating conditions.
Keywords
Markov jump linear systems, microgrid, direct-data driven control
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Semester Project , Master Thesis
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Published since: 2024-11-16 , Earliest start: 2025-01-01
Applications limited to Empa , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich
Organization Automatic Control Laboratory
Hosts 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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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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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
Optimal Control of Plants in Hydroponic Systems
This project deals with the optimal control of crops in a hydroponics system. A hydroponics system is a controlled environment in which crops grow in a nutrient solution instead of soil. The goal is to design an algorithm that leverages data to optimally control the environmental conditions of the crop. The objective is to achieve a fast crop growth with as little as possible energy investments.
Keywords
Control Theory, Formal Methods, Agriculture, Hydroponics, Safety, Stochastic Systems, Reinforcement Learning
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Master Thesis
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Published since: 2024-11-15
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Wallington Kevin , Schmid Niklas
Topics Mathematical Sciences , Information, Computing and Communication Sciences , 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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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.
Keywords
Game Theory, Learning, Data Analysis, Energy Market
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Master Thesis
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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.
Keywords
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.
Keywords
Game Theory, Mechanism Design, Bounded Rationality, Learning in Games
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Master Thesis
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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
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: 2024-10-18
Organization Automatic Control Laboratory
Hosts Eising Jaap
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Fast Computation of Dynamic Population Games with Madupite
Dynamic Population Games (DPGs) are an important class of games that models many real-world problems, including energy systems, epidemics, and the recently proposed “karma economies” for fair resource allocation. A DPG consists of a large population of self-interested agents each solving an individual Markov Decision Process (MDP). The MDP of each agent is coupled to the actions of others and is hence parametrized by the policies adopted in the population. Computing the Nash equilibrium of a DPG is challenging as it involves iteratively solving MDPs many times. This suffers from the well-known curse of dimensionality which severely limits the size of the state and action spaces that are computationally tractable. Madupite is a novel distributed high-performance solver for large-scale infinite horizon discounted MDPs, which leverages PETSc to implement inexact policy iteration methods in a distributed fashion. Despite its software complexity, Madupite comes with a very intuitive Python interface and a detailed documentation, that allow any Python user to easily deploy it to efficiently simulate and solve large-scale MDPs in a fully distributed fashion. Preliminary benchmarks have showcased the great potential of Madupite, which is capable of efficiently handling MDPs with millions of states. Motivated by the recent development of Madupite, this project aims at developing fast computation tools that are capable of solving large-scale DPGs.
Keywords
Programming (Python/C++), Markov Decision Processess, Game Theory, Karma Economy
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Semester Project , Bachelor Thesis
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Published since: 2024-09-25 , Earliest start: 2024-09-30
Organization Automatic Control Laboratory
Hosts Elokda Ezzat , Gargiani Matilde
Topics Information, Computing and Communication Sciences , Engineering and Technology
Contact
ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland