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
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
Safe Flow Matching with Control Barrier Functions
This project investigates safety-aware flow-based generative modeling for control and robotics. Flow matching provides an efficient continuous-time framework for generating trajectories, actions, and structured decisions, but standard methods do not guarantee that generated outputs satisfy safety or feasibility constraints. To address this limitation, the project explores the integration of Control Barrier Functions into flow-based generative models, aiming to enforce state and input constraints during generation and downstream execution. The goal is to develop a principled framework that combines the expressiveness of flow matching with formal safety guarantees, enabling reliable trajectory and decision generation for safety-critical autonomous systems.
Keywords
Flow Matching, Safe Control, Control Barrier Functions
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-04-27 , Earliest start: 2026-05-04
Applications limited to ETH Zurich , University of Zurich
Organization Automatic Control Laboratory
Hosts Wang Han
Topics Information, Computing and Communication Sciences
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.
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Master Thesis
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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.
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Master Thesis
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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.
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Master Thesis
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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.
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Master Thesis
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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.
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Master Thesis , Theory (IfA) , Computation (IfA) , Energy (IfA) , Applications (IfA)
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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
Multi-Agent Grid Impedance Identification in Three-Phase Power Systems
This project investigates multi-agent grid impedance identification in three-phase power systems. It will develop and compare two approaches: a global multi-port identification framework and a locally simultaneous multi-agent single-port identification framework. The study will evaluate and compare the accuracy of the two approaches and explore potential downstream applications in stability analysis and control design.
Keywords
grid impedance, system identification, multi-agent systems, three-phase power system dynamic modelling
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Master Thesis
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Published since: 2025-12-31 , Earliest start: 2026-01-01
Organization Automatic Control Laboratory
Hosts Häberle Verena
Topics Engineering and Technology
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ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland