Automatic Control Laboratory

Open Opportunities

Identifying influencers in social networks ETH Zurich Automatic Control Laboratory 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. Engineering and Technology, Mathematical Sciences Master Thesis, Semester Project

Applications of Iterative Learning Control ETH Zurich Automatic Control Laboratory 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. Electrical and Electronic Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering Applications (IfA), Master Thesis, Semester Project

Feedback Optimization for Freeway Ramp Metering ETH Zurich Automatic Control Laboratory 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. Electrical Engineering Master Thesis, Semester Project

Divergence for convergence: Stability guarantees via Bregman divergence ETH Zurich Automatic Control Laboratory 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. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Hopping (and Hoping) for Stability: Data-Driven Control of Microgrids with Markov Jumps ETH Zurich Automatic Control Laboratory 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. Engineering and Technology Master Thesis, Semester Project

Experimental Validation of an Impedance Identification Method for Three-Phase Power Systems ETH Zurich Automatic Control Laboratory 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. Engineering and Technology Master Thesis

Optimal Excitation for Grid Impedance Estimation ETH Zurich Automatic Control Laboratory This project aims to develop optimal excitation schemes for impedance estimation of grid/grid-connected converters. Engineering and Technology Master Thesis

Enhancing Model Predictive Control with Reinforcement Learning ETH Zurich Automatic Control Laboratory 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. Systems Theory and Control, Systems Theory and Control Master Thesis

Conducting an Orchestra: Learning in Stackelberg Games with Maestro ETH Zurich Automatic Control Laboratory 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. Applied Economics, Applied Statistics, Numerical Analysis, Optimisation, Simulation and Modelling Master Thesis

Data-driven Control in Building Energy Systems ETH Zurich Automatic Control Laboratory 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. Control Engineering, Electrical Engineering, Interdisciplinary Engineering, Mechanical Engineering, Systems Theory and Control Master Thesis