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
Structured State-space modelling and control of Pipette systems
Precision liquid handling is essential in laboratory automation, biotechnology, and medical diagnostics, where accurate volume dispensing is critical. However, achieving reliable performance remains challenging due to complex physical effects such as friction, pressure oscillations, evaporation, and varying liquid properties. This project aims to develop a data-driven modeling and control framework for precision pipetting systems using Structured State-Space Models (SSMs) and modern control techniques. The student will learn dynamical models directly from experimental data, design robust feedback controllers, and investigate state-estimation methods for unmeasured variables. Particular attention will be given to robustness and performance guarantees under uncertainty. The developed methods will be validated through simulation and experiments on a real industrial platform provided by Hamilton Robotics. The project offers hands-on experience at the intersection of machine learning, system identification, and control, with applications to next-generation laboratory automation systems.
Keywords
state-estimation, system-identification, learning-based control, fluid dynamics, SSMs, Mamba, sequence modelling
Labels
Semester Project , Master Thesis
PLEASE LOG IN TO SEE DESCRIPTION
More information
Published since: 2026-06-05 , Earliest start: 2026-09-01 , Latest end: 2026-12-01
Organization Automatic Control Laboratory
Hosts Zakwan Muhammad
Topics Mathematical Sciences , Information, Computing and Communication 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
Labels
Master Thesis , ETH Zurich (ETHZ)
PLEASE LOG IN TO SEE DESCRIPTION
More information
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
Labels
Master Thesis , ETH Zurich (ETHZ)
Description
Goal
Contact Details
More information
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
Labels
Semester Project , Master Thesis
More information
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
Labels
Master Thesis , ETH Zurich (ETHZ)
Description
Goal
Contact Details
More information
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
Labels
Master Thesis , ETH Zurich (ETHZ)
Description
Goal
Contact Details
More information
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
Labels
Master Thesis , ETH Zurich (ETHZ)
Description
Goal
Contact Details
More information
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.
Labels
Master Thesis
More information
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
Labels
Semester Project , Master Thesis
Description
Goal
Contact Details
More information
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
Labels
Master Thesis
PLEASE LOG IN TO SEE DESCRIPTION
More information
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
Verifiable Latent Space Control Design Beyond Stability and Forward Invariance
Low-dimensional latent space representations of dynamical systems provide a powerful tool for scalable control design. Over the last years, data-driven approaches for constructing latent space representations have gained popularity and shown great empirical success. While these methods are promising, they typically lack formal control guarantees as needed in safety-critical applications. In recent work, we provided a theoretical framework for designing controllers in such learned latent spaces that can provably guarantee stability and safety for the original system by exploiting approximate conjugacy between the latent and full dynamics. Yet, these guarantees are largely limited to using notions of Lyapunov and barrier functions, ensuring stability and forward invariance only. Modern autonomous control systems, however, must often satisfy richer temporal or logical specifications that involve deadlines, sequencing, and reactive behavior. This thesis will investigate how to extend our latent space control design framework beyond stability and forward invariance, towards achieving verifiable temporal and logic-based system behavior. The project combines insights from representation learning, formal methods, and control theory, aiming to unify latent space learning with verifiable control design.
Labels
Master Thesis
More information
Published since: 2026-04-03 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Information, Computing and Communication Sciences , Engineering and Technology
Interaction-Aware Control Design with Provable Guarantees in Realistic Robotic Scenarios
This project builds on the guarantees introduced in to design, analyze, and validate an interaction-aware control stack for robotic systems operating among responsive agents. The approach couples an internal trajectory-prediction module that is explicitly updated as policies change with a distribution-free safety layer based on conformal tools that retain finite-sample coverage in the presence of feedback-induced shift and adversarial perturbations. The resulting uncertainty sets are composed with certified planning and safety filters such as control barrier functions and reachability-based shielding. Evaluation encompasses software simulation and on-robot trials in realistic interactive scenarios and compares against confidence-aware and reachability/ORCA-style baselines.
Labels
Master Thesis
More information
Published since: 2026-04-03 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Information, Computing and Communication Sciences , Engineering and Technology
Probabilistically Safe Motion Planning in Uncertain Dynamic Environments
Autonomous robots operating in dynamic environments must plan around obstacles with uncertain future trajectories, such as pedestrians or vehicles. Existing motion planning approaches either ignore this uncertainty—risking collisions—or rely on heuristic safety margins, leading to overly conservative behavior without formal guarantees. This work establishes a principled, data-driven framework that integrates conformal prediction with the augmented Graph of Convex Sets (GCS) motion planning paradigm. The key insight is that the H-representation of obstacles in spacetime GCS naturally aligns with conformal prediction sets. By scaling the polytope constraints using conformal quantiles calibrated from trajectory data, we construct spacetime uncertainty sets with finite-sample coverage guarantees. Coupling these probabilistically certified sets with deterministic GCS planning yields end-to-end safety guarantees: the probability of collision is bounded by a user-specified risk level. This approach enables tunable, statistically grounded safety in motion planning under uncertainty.
Labels
Master Thesis
More information
Published since: 2026-04-03 , Earliest start: 2026-04-03 , Latest end: 2027-02-06
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Information, Computing and Communication Sciences , Engineering and Technology
Runtime Monitoring with Formal Specification-Guided LLMs
Autonomous systems increasingly rely on complex software stacks and data-driven components whose behavior is difficult to fully verify before deployment. Runtime verification provides a lightweight mechanism for ensuring that a system execution satisfies formally specified properties \cite{lindemann2023conformal,bauer2011runtime,lukina2021into}. Classical runtime monitors are typically symbolic and algorithmically constructed from temporal logic specifications. These properties have to be specified a-priori by a domain expert, posing a practical bottleneck. This thesis investigates a novel paradigm in which the runtime monitor itself is implemented as a Large Language Model (LLM) so that the system specification can be provided via a natural language interface. While this has the advantage of not requiring expert knowledge and being able to change specifications on-the-fly, it is unclear how reliable such a monitoring approach would be, which necessitates additional formal structure on the problem formulation and implementation.
Labels
Master Thesis
More information
Published since: 2026-03-09 , Earliest start: 2026-03-10 , Latest end: 2026-12-31
Organization Automatic Control Laboratory
Hosts Lindemann Lars , Balta Efe
Topics Engineering and Technology
De-bugging and Tuning of Learning Based Controllers
This project focuses on developing an autonomous debugging and tuning system for self-commissioning controllers in HVAC applications. These controllers automatically learn system dynamics and configure PI gains, but their performance depends on precise hyperparameter settings and adaptive tuning to address issues like misconfiguration or changing conditions. The goal is to create a program that analyzes controller behavior in real time, detects performance issues, and autonomously adjusts parameters for optimal operation. Key tasks include identifying critical hyperparameters, defining performance metrics, and designing a robust tuning algorithm. Numerical simulations will validate the approach across diverse scenarios, aiming for true plug-and-play functionality—enabling controllers to self-monitor and adapt like an expert engineer. Ideal for students with a background in control systems or automation, the project offers hands-on experience in adaptive control, system identification, and smart building technologies. Proficiency in MATLAB/Simulink and basic machine learning knowledge are beneficial. The project will be co-supervised with Belimo Automation AG.
Keywords
Self-learning controllers, Control systems, PI control, Hyperparameters, System identification, Plug-and-play control, Debugging, Tuning, Smart buildings, HVAC, Adaptive control, Simulation and validation.
Labels
Master Thesis , Theory (IfA) , Computation (IfA) , Energy (IfA) , Applications (IfA)
Description
Goal
Contact Details
More information
Published since: 2026-03-02 , Earliest start: 2026-03-01 , Latest end: 2026-08-31
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne , Empa , Eawag , Lucerne University of Applied Sciences and Arts , University of Basel , University of Berne , University of Fribourg , University of Geneva , University of Lausanne , University of Lucerne , University of St. Gallen , University of Zurich , Zurich University of Applied Sciences
Organization Automatic Control Laboratory
Hosts Balta Efe
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Contact
ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland