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
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
No-Regret Zero-Shot Meta Reinforcement Learning for Control-Affine Systems
Offline reinforcement learning typically assumes access to a simulator capable of generating training tasks from a fixed distribution. However, despite potentially broad training coverage, real-world deployment often presents environments that are unique and never encountered during training. This mismatch is especially pronounced when policies trained offline in simulation are deployed on physical systems, where online episodic training is impractical or unsafe. In such scenarios, agents must adapt rapidly to a new environment using a single trajectory, operating in a zero-shot or one-shot setting. This thesis aims to develop no-regret algorithms for zero-shot meta reinforcement learning using a grey- box modeling approach. We assume partial knowledge of the system dynamics—specifically, the functional structure of the model—while treating the system parameters as unknown. This abstraction enables principled adaptation at test time and facilitates the derivation of rigorous performance guarantees. Crucially, it also allows the incorporation of safety and stability constraints, which are essential for deployment in cyber-physical systems. The focus of the project is on control-affine systems, which arise frequently in practical applications. The thesis investigates how bilinear and control-affine quadratic control problems, subject to additive non- stochastic disturbances, can be formulated within a zero-shot reinforcement learning or online optimization framework. Building on this formulation, the goal is to derive state-of-the-art online controllers that achieve low dynamic regret while ensuring robustness and stability. The proposed methods will be evaluated both through simulation studies and rigorous theoretical analysis, with guarantees in terms of bounded-input bounded-state (BIBS) stability and dynamic regret.
Keywords
Reinforcement Learning, Meta Learning, Online Optimization, Artificial Intelligence, Fine Tuning, Adaptive Control
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Master Thesis
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Published since: 2026-02-23 , Earliest start: 2026-03-01 , Latest end: 2026-10-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization Automatic Control Laboratory
Hosts Karapetyan Aren
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Robust Feedback Control for Robotic Carton Folding
This project aims to develop control strategies for robotic carton folding that explicitly regulate the evolution of the carton state during manipulation. Controlling articulated and partially deformable objects remains an open problem in robotic manipulation, particularly in contact-rich tasks such as carton folding, where success depends on precise coordination of motion, force, and compliance. The overall objective is to investigate control formulations that enable robust, generalizable folding behaviors beyond open-loop or purely trajectory-based execution.
Keywords
Robotics, Force Control, Vision, Hardware
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Semester Project , Master Thesis
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Published since: 2026-02-19 , Earliest start: 2026-02-23 , Latest end: 2027-02-23
Organization Automatic Control Laboratory
Hosts Stocco Paula
Topics 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
Safe Feedback Optimization for Power Grids via Switched Controllers
Feedback optimization is emerging as an important control method for modern power systems, thanks to its robustness and ability to steer the grid to an efficient operating point. Clearly, power systems are safety-critical infrastructures: failures can cause severe consequences, such as blackouts. In this project, we will design and evaluate novel feedback optimization schemes, based on switched systems, which can guarantee safety of the grid at all times.
Keywords
Computational control, feedback optimization, switched systems, smart grid, projected dynamics
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Semester Project , Master Thesis
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Published since: 2025-12-04 , Earliest start: 2026-01-05
Applications limited to ETH Zurich , University of Zurich
Organization Automatic Control Laboratory
Hosts Bianchi Mattia
Topics Mathematical Sciences
Unsupervised anomaly detection in additive manufacturing using flows
This thesis advances in-situ process monitoring in Laser Powder Bed Fusion (PBF-LB/M) by integrating Eddy Current Testing (ECT) and computer vision (CV) for defect detection. The study focuses on signal interpretation across simple to complex geometries, using multi-sensor data fusion to improve defect identification with real world data.
Keywords
Sensor fusion, machine learning, anomaly detection, additive manufacturing, computer vision, Neural Flows
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Master Thesis
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Published since: 2025-11-18 , Earliest start: 2026-02-16 , Latest end: 2026-05-29
Organization Automatic Control Laboratory
Hosts Zakwan Muhammad
Topics Information, Computing and Communication Sciences , Engineering and Technology
AI for Grid Stability: Deep Learning the Small-Signal Dynamics of Power Converters
This project focuses on developing and validating a scalable machine learning framework to address the modeling challenges of small-signal stability assessment in grid-connected converters.
Keywords
Data-driven modelling, Machine Learning, Power Converters, Small-signal modelling, Sub-synchronous oscillations, Control, Stability
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Master Thesis
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Published since: 2025-11-18
Organization Automatic Control Laboratory
Hosts Abdalmoaty Mohamed
Topics Engineering and Technology
Distribution-Free State Estimation for Dynamical Systems via Conformal Prediction
Accurate state estimation in dynamical systems is traditionally achieved with model-based filters and smoothers such as the Kalman filter (KF), particle filters (PF), and Rauch-Tung-Striebel (RTS) smoothers. These methods offer optimal state estimates under specific probabilistic assumptions (e.g. linear Gaussian models) and provide transparency via interpretable models and theoretical guarantees (e.g. MMSE optimality of KF under Gaussian noise). However, real systems often violate these assumptions as noise can be heavy-tailed or unknown and models mismatched leading to overconfident or inaccurate uncertainty estimates. Conformal prediction (CP) provides distribution-free uncertainty quantification with finite-sample coverage guarantees and minimal modeling assumptions. This thesis explores estimation algorithms that combines the transparency and structure of model-based filters/smoothers with CP's distribution-free coverage, studying theory and empirical behavior to obtain the best of both worlds for generic dynamical systems.
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Master Thesis
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Published since: 2025-11-18 , 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: 2025-11-18 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Information, Computing and Communication Sciences , Engineering and Technology
Safe Manipulation in Complex Environments
Dynamical-system approaches for robotic control reshape velocity fields around obstacles for im- pressive reactive avoidance. Yet, they can still encounter unwanted local minima and saddle points. Research on Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) has estab- lished conditions under which such equilibria arise and has provided controller-synthesis methods to avoid them. By incorporating these safety and stability frameworks into DS approaches we hope to introduce new solutions for real-time task execution in cluttered, real-world settings.
Keywords
Robotics, Collision Avoidance
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ETH Zurich (ETHZ)
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Published since: 2025-11-17 , Earliest start: 2026-01-01 , Latest end: 2026-08-01
Organization ETH Zurich
Hosts Stocco Paula , Wang Han
Topics Mathematical Sciences , Information, Computing and Communication Sciences
Verifiable Control Design with Data-Driven Spectral Submanifolds
Low-dimensional latent space representations of dynamical systems provide a powerful tool for scalable control design. Over the last years, learning-based approaches for constructing latent space representations have gained popularity, often utilizing variational autoencoders. While these approaches have shown great empirical success, they typically lack formal control guarantees as needed in safety-critical applications. In recent work, we presented a 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. However, large approximate conjugacy can result in overly conservative and poorly performing control actions. On the other hand, spectral submanifolds were studied in as attracting invariant manifolds. Spectral submanifolds guarantee the existence of a low-dimensional latent space representation that, if restricted to the manifold, have the same behavior as the original system. Spectral submanifolds are constructed from the linear components of a dynamical system and have zero conjugacy error, even though their data-driven construction is approximate in nature. This thesis will investigate how to integrate the framework of spectral submanifolds into the latent space control design framework from \cite{lutkus2025latent} to achieve non-conservative control behavior while retaining stability and safety guarantees. The project combines insights from dynamical systems, control theory, and data-driven reduced order models.
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Master Thesis
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Published since: 2025-11-17 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Lindemann Lars
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.
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Master Thesis
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Published since: 2025-11-17 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Information, Computing and Communication Sciences , Engineering and Technology
Deep reinforcement learning for job shop scheduling
Production scheduling is a critical event in the production management of smart manufacturing systems. Many job shop scheduling (JSP) problems are known as NP-hard. Meanwhile, in the real production plants, not only is the objective of scheduling usually multiple (e.g., makespan, tardiness, machine utilization), but also the environment is mostly dynamic with unexpected machine breakdowns, order arrivals and cancellations, etc. Because the JSP can be modelled as a Markov Decision Process (MDP), using deep reinforcement learning techniques for scheduling has gained significant attention in this domain. In this project, a deep reinforcement learning environment for JSP will be developed and enhanced to tackle the aforementioned challenges.
Keywords
Deep reinforcement learning, Job shop scheduling, Operational research, Multi-objective optimization
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Semester Project , Master Thesis
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Published since: 2025-11-15 , Earliest start: 2026-01-01 , Latest end: 2026-09-01
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Tang Yu, Mr.
Topics Engineering and Technology
Temporally Robust Controller Synthesis for Time-Critical Systems
The reliability of autonomous control systems depends on their robustness, i.e., their ability to meet their objectives under uncertainty. Emerging applications in autonomy are increasingly time-critical: failing to meet temporal requirements can severely compromise safety and performance, as in multi-robot disaster response, fleets of autonomous taxis, and automated airport ground control. Yet today’s technology still struggles with unpredictable delays and coordination failures, limiting their reliability in real-world settings. Existing research has focused on spatial robustness, ensuring that spatial objectives - such as collision avoidance of a robot - are met despite modeling errors and disturbances. However, time-critical systems also require temporal robustness, the ability to meet time-critical objectives - such as deadlines, sequencing, and periodic tasks - under uncertainty. A major challenge arises from variations in computation and actuation times, and particularly from complex timing uncertainties caused by human interaction, unpredictable sensing failures, and compute-intensive perception. This thesis will study novel ways to enable temporal robustness of autonomous control systems.
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Master Thesis
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Published since: 2025-11-14 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Lindemann Lars
Topics Information, Computing and Communication Sciences , Engineering and Technology
Feedback Control for Learning in Optimization and Game Theory
How can we design learning dynamics that are fast, robust, and provably correct when many decision-makers interact under shared constraints? This project investigates this question by leveraging a novel control-theoretic framework for equality-constrained optimization to design and analyze dynamics that compute solutions to Generalized Nash Equilibrium problems in both static and time-varying settings. Using contraction theory as a scalable framework for robust stability analysis, the goal is to derive verifiable conditions for global exponential convergence and robustness to disturbances, leading to algorithms with provable performance guarantees.
Keywords
Generalized Nash Equilibrium, Contraction Theory, Primal--Dual Dynamics, Feedback Control, Time-Varying Games
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Master Thesis
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Published since: 2025-11-14 , Earliest start: 2025-12-01
Applications limited to ETH Zurich , University of Zurich
Organization Automatic Control Laboratory
Hosts Centorrino Veronica
Topics Mathematical Sciences
Fair Demand-Side Flexibility Allocation with Karma
The rapid integration of distributed renewable energy sources into electric power grids introduces the challenges of balancing intermittent power production and guaranteeing grid stability. Demand-side energy flexibility, which involves the end-user shifting or adjusting energy consumption in line with the supply and capacity of the electricity grid, is receiving increasing attention as an important approach towards tackling these challenges. Buildings are significant energy consumers and hence present a promising source of flexibility. Leveraging the thermal energy storage capabilities of buildings allows to intentionally modify their energy consumption to support grid needs without compromising occupants’ thermal comfort. Previous work has focused on quantifying energy flexibility envelopes or designing temperature control strategies which are important to meet flexibility objectives. However, in practice, Distribution System Operators (DSOs) often prefer to provide flexibility targets to groups of buildings rather than to individual units. This leads to the question of how to fairly and efficiently distribute flexibility targets to individual households within a designated group. This project aims to develop a karma mechanism for this flexibility allocation problem. Karma economies are a promising recently developed non-monetary solution to the allocation of shared resources. These economies leverage the fact that shared resources, such as flexibility requests, do not occur once but are repeated frequently. It is thus envisioned that karma will enable consumers to express when they have high urgency to avoid shifting their consumption, meanwhile guaranteeing that everyone contributes fairly to providing flexibility over time.
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Master Thesis
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Published since: 2025-11-12 , Earliest start: 2025-12-01
Organization Automatic Control Laboratory
Hosts Elokda Ezzat , Nortmann Benita , Montazeri Mina
Topics Engineering and Technology
Online Feedback Optimization for Real Time Power Grid Control
Online Feedback Optimization (OFO) allows to drive a dynamical system to a steady state that satisfies given optimality criteria, such as efficiency and satisfactions of constraints. In the past years, we had great success in applying OFO to the real-time control of power grids, both for energy distribution and transmission. With RTE France we simulated how to solve grid congestion when there is an excess of wind generation. We deployed an OFO algorithm on the Swiss grid to control the voltage and the reactive power of a distribution grid. And we won the Watt D'Or award by the Swiss Federal Office of Energy!
Keywords
smart grid, optimization, control of power grids, feedback optimization, energy
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-11-11 , Earliest start: 2026-01-01
Organization Automatic Control Laboratory
Hosts Bolognani Saverio
Topics Information, Computing and Communication Sciences
A Game Plan for Electricity: Optimal Incentive Design for Procuring Voltage Control in Transmission Grids
The reliable operation of modern power systems increasingly relies on flexible and distributed resources such as renewable generators, battery storage, and demand-side participants. These resources can provide essential control services such as frequency and voltage support. As many of these resources participate voluntarily, system operators must design incentive mechanisms that encourage participation while ensuring safe and stable grid operation. Existing approaches for procuring and coordinating such services often lack formal guarantees regarding stability, robustness, and economic efficiency, especially under uncertainty in system dynamics, participant behavior, or external disturbances. This creates an opportunity to develop new incentive and control strategies that combine theoretical rigor with practical applicability.
Keywords
Incentive Design, Game Theory, Power Grid, Energy Systems, Swissgrid, Voltage, Control, Optimization
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-10-29
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Matt Jonas
Topics Mathematical Sciences , Engineering and Technology
Reading the Game: Predicting the Behavior of Participants in Swissgrid’s Voltage Control Program via Inverse Optimization and Inverse Game Theory
The secure and efficient operation of the Swiss transmission grid relies on the cooperation of connected agents, such as power plants and distribution system operators (DSOs), which can provide voltage support through reactive power control. To promote this behavior, Swissgrid offers financial incentives via its voltage support program. However, operational data shows that participants react very heterogeneously to these incentives. The reasons for this diversity are unclear, as the agents’ cost structures, technical limits, and strategic motivations are not directly observable. Understanding this behavioral diversity is key to designing more effective and equitable incentive mechanisms for future power systems.
Keywords
Inverse, Optimization, Game Theory, Control, Incentive Design, Learning, Stackelberg, Leader, Follower, Bilevel, Swissgrid, Power Grid, Energy Systems
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Semester Project , Bachelor Thesis , Master Thesis
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Published since: 2025-10-29
Applications limited to ETH Zurich
Organization Automatic Control Laboratory
Hosts Matt Jonas
Topics Mathematical Sciences , Engineering and Technology
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ETH Zurich
Automatic Control Laboratory
Physikstrasse 3
8092 Zurich
Switzerland