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
Physics-Consistent Lifelong Learning and Adaptive Control for Energy-Efficient Ovens
Modern household ovens are expected to provide high cooking performance while minimizing energy consumption throughout their lifetime. However, the thermal characteristics of an oven gradually change due to aging effects such as insulation degradation, heating element wear, and sensor drift, causing conventional factory-calibrated models and controllers to lose accuracy over time. This thesis investigates a novel lifelong learning framework that enables ovens to continuously adapt to these changes by combining physics-consistent machine learning with adaptive control techniques. The project will develop thermodynamic models that incorporate known physical principles, employ Bayesian optimization for automatic calibration, and use online system identification to continuously update the model using operational data. The learned models will then be integrated into adaptive control algorithms that automatically adjust controller parameters to maintain temperature regulation accuracy while improving long-term energy efficiency. The proposed methods will be validated using simulation, industrial datasets, and available hardware at V-ZUG, with the possibility of deployment on a real oven. This project provides an opportunity to work at the intersection of control theory, machine learning, system identification, and industrial automation on a real-world problem with significant practical impact.
Keywords
state-estimation, system-identification, learning-based control, thermodynamics
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Master Thesis
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Published since: 2026-06-18 , Earliest start: 2026-09-01 , Latest end: 2027-03-01
Organization Automatic Control Laboratory
Hosts Zakwan Muhammad
Topics Mathematical Sciences , Engineering and Technology
The frequency safety control of large-scale power electronics-dominated power systems
The increasing penetration of inverter-based renewable energy resources is transforming the frequency regulation paradigm of modern power systems. Reduced system inertia, distributed frequency-support resources, and emerging spatial frequency variations pose significant challenges to maintaining secure system operation. This project develops a distributed safety-critical control framework for low-inertia power systems based on Control Barrier Functions. The proposed approach aims to guarantee nodal frequency safety by ensuring that all bus frequencies remain within prescribed operational limits throughout transient evolution. By exploiting the sparse network structure of power systems, global frequency-safety requirements are decomposed into local safety conditions that can be enforced through distributed control actions and limited information exchange. The resulting framework combines rigorous safety guarantees with scalable real-time implementation, providing a promising solution for frequency-safe operation of future power-electronics-dominated power systems.
Keywords
Safe Control, Power Systems, Frequency Control, Multi-Agent Systems
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-06-04 , Earliest start: 2026-06-15 , Latest end: 2027-04-01
Organization Automatic Control Laboratory
Hosts Zhuang Kehao , Wang Han
Topics Engineering and Technology
Probabilistic Forecasting for Predictive Maintenance
Predictive maintenance asks a simple question with high stakes: when should a machine be serviced before it actually breaks? Getting the answer wrong is expensive either way, since unplanned failures cause downtime while servicing too early wastes parts and labor. Despite decades of work, the problem is far from solved. Two difficulties stand out. First, sensor signals span very different timescales, with wear accumulating over months but the most informative measurements living at high frequency. Second, any maintenance decision is taken under genuine uncertainty about the future, and a safe decision requires that uncertainty to be made explicit. This thesis explores how to combine modern long-context sequence models, such as Transformers and state-space models like Mamba, with probabilistic forecasting techniques, to produce calibrated predictions of future machine health that can drive safer and more efficient maintenance decisions on standard public datasets.
Keywords
Predictive Maintenance, Probabilistic Forecasting, Time Series, State-Space Models, Uncertainty Quantification
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-06-03 , Earliest start: 2026-06-01
Applications limited to University of Zurich , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Automatic Control Laboratory
Hosts Delcaro Giacomo , Zakwan Muhammad
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Welfare based evaluation of autonomous vehicle integration in Zurich
The City of Zurich has identified several metric groups for assessing autonomous vehicle (AV) integration, including allocation of urban mobility space and time, safety, environment and livability, traffic performance and reliability, and equity and access. This project asks how these metrics should be turned into a clear evaluation framework for policy analysis.
Keywords
Urban mobility, autonomous vehicles, optimization, policy evaluation
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Semester Project , Master Thesis
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Published since: 2026-06-02 , Earliest start: 2026-06-03 , Latest end: 2027-02-01
Organization Automatic Control Laboratory
Hosts Shilov Ilia
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Economics , Policy and Political Science
Meta-Learning transformer policies for control of non-linear systems
What if, instead of designing a controller for one specific plant, we trained a single policy that already knows how to control an entire family of plants? This is the promise of meta-learning for dynamical systems: a policy is trained once, offline, on a randomized class of simulated systems, and is then deployed zero-shot on the real hardware, with no on-system tuning. Recent work has shown that this idea can be realized with Transformer architectures through a mechanism called In-Context Learning (ICL), and has been validated on a physical brushless DC motor. This thesis builds on that line of work and proposes a decoupled architecture that explicitly separates system identification from control inside the policy. The framework will be developed in simulation, extended to nonlinear plants and generic reference trajectories, and validated experimentally in our labs.
Keywords
Meta-Learning, In-Context Learning, Transformers, Adaptive Control, Sim-to-Real
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-05-30 , Earliest start: 2026-06-01
Applications limited to University of Zurich , ETH Zurich
Organization Automatic Control Laboratory
Hosts Delcaro Giacomo
Topics Information, Computing and Communication Sciences , Engineering and Technology
Multi-Fidelity Bayesian Optimization with Parallel Information Sources
Many engineering and machine-learning problems boil down to the same question: which settings make this system work best? Tuning a controller, picking the hyperparameters of a neural network, or finding a good policy for a robot all require optimizing a performance metric whose value can only be measured by a slow, costly, or risky experiment. Bayesian Optimization (BO) is the standard tool for this setting, but it traditionally relies on a single, expensive source of information, even when cheaper approximations (simulators, reduced-order models, short training runs) are readily available. In this Master's thesis you will design and implement a new BO framework that can leverage many sources of information in parallel, each with its own accuracy and computational cost, and that learns online which sources are worth querying and which to drop. The work is explicitly scoped to lead to a co-authored publication at a top ML venue.
Keywords
Machine Learning, Bayesian Optimization, Gaussian Process, Multi-Task Optimization
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-05-30 , Earliest start: 2026-06-01
Applications limited to University of Zurich , ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Automatic Control Laboratory
Hosts Delcaro Giacomo
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Preferential Bayesian Optimization with Cost Priors, Constraints, and LLM-Assisted Operator Interfaces
Almost every problem in modern control and machine learning eventually reduces to minimizing a cost function. In practice this cost combines several competing objectives — tracking accuracy, energy consumption, safety margins — whose relative weights are usually chosen by hand, with little principled justification. Preferential Bayesian Optimization (PBO) offers a more systematic alternative: rather than fixing the cost in advance, the system shows the user two candidate outcomes, asks which one is preferred, and progressively learns a surrogate of the user's true preferences from these comparisons. The same mechanism underlies the alignment of modern large language models (LLMs) to human feedback. This thesis extends PBO along three directions: incorporating structured prior knowledge about the cost components, handling constraints on individual objectives, and coupling the framework with an LLM-based conversational interface, translating natural-language instructions into structured updates to the optimizer.
Keywords
Machine Learning, Bayesian Optimization, Preference Optimization, LLM
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Semester Project , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-05-29 , Earliest start: 2026-06-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , University of Zurich
Organization Automatic Control Laboratory
Hosts Delcaro Giacomo
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Safe and Performance-Aware Reinforcement Learning for Legged Robots
Reinforcement learning has become a powerful tool for robotic locomotion, including legged robots, humanoids, and wheel-legged systems. Modern simulators such as NVIDIA Isaac Sim and Isaac Lab make it possible to train policies with algorithms such as proximal policy optimization (PPO) in high-fidelity, GPU-accelerated environments. However, learned policies often rely on reward engineering and may exhibit poor transient behavior, limited robustness, or safety violations under disturbances, actuator limits, and model mismatch. This thesis will investigate how tools from nonlinear control theory can be combined with modern reinforcement learning to improve the reliability of learned locomotion policies. In particular, the project will study prescribed performance control (PPC), control Lyapunov functions (CLFs), and control barrier functions (CBFs) as mechanisms for reward shaping and, where feasible, lightweight online action filtering. The main application will be wheel-legged robotic locomotion, with possible extensions to wheeled-biped or humanoid robots in Isaac Sim / Isaac Lab.
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Master Thesis
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Published since: 2026-05-26 , Earliest start: 2026-05-27 , Latest end: 2027-09-10
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Automatic Calibration Procedure for a 5-Axis Robotic 3D Printer
Additive manufacturing systems are typically operated in an open-loop fashion, where both motion and material extrusion are precomputed offline. This makes the process sensitive to disturbances such as geometric misalignments, material variations, and changes in process conditions. Accurate geometric calibration, such as bed leveling and multi-axis alignment, is therefore an important prerequisite for reliable and repeatable printing. This is especially relevant for 5-axis additive manufacturing systems, where additional rotational axes introduce further calibration challenges. This project focuses on improving the reliability of a custom-built 5-axis 3D printer at IfA by developing automated calibration and startup procedures.
Keywords
Additive Manufacturing, Calibration, 5-Axis Systems, Robotics, Geometric Accuracy
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Semester Project , Master Thesis
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Published since: 2026-05-22 , Earliest start: 2026-06-01 , Latest end: 2027-02-28
Organization Automatic Control Laboratory
Hosts Seckin Ilyas
Topics Engineering and Technology
Feedback control for the first Swiss local energy markets
How can one safely control an electricity grid with multiple selfish stakeholders such as electric vehicles owners and solar panel owners, in real time? This project investigates this question for Walenstadt, a Swiss town where "the grid of tomorrow" is currently being created. The goal is to test PRIME, a recently proposed feedback market mechanism that controls the grid by providing economic incentives to the stakeholders, to drive their decision-making and achieve coordination. The tests are performed in simulation on a realistic model, but tests in the real grid of Walenstadt are a possibility if the simulations are successful.
Keywords
Control, energy markets, feedback optimization
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Master Thesis
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Published since: 2026-05-17 , Earliest start: 2026-05-24
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Bianchi Mattia
Topics Engineering and Technology
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ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland