Student Projects

The EPFL babyfoot is under continuous improvement.

While the babyfoot can easily intercept the ball and kick toward the opponent goal with success, it can also juggle and shoot if the ball is still.

The difficulty is to capture the ball, make a pass and chain precise actions. This project aims at improving the ball handling while it is moving to permit capture, pass and juggling, and then shoot toward opponents goal.

Students suggested improvements are also welcome.

The babyfoot is programmed using LabVIEW, knowledge of this language is prerequisite for strategy related projects.

Professor(s)

Christophe Salzmann (Laboratoire d’automatique 3)

Site

https://www.epfl.ch/labs/la/pi/babyfoot/

The environmental and economic significance of wind power in the 21st century is mainstream knowledge The aim of the project is the implementation of a robust Model Predictive Control (MPC) scheme for Wind Farms (WF) capable of dealing with noise uncertainties inherently present in the. At the same time, the controller should fit the required power, maximize the performance and minimize the mechanical forces acting on the turbines. The student will leverage the new results in Wasserstein Tube MPC [1] which is an extension of the well-known Tube MPC. This new approach is less conservative than the standard techniques used in robust control which leads to better performances of the closed-loop.

Skills needed:

Coding proficiency in Matlab (!!!)

Understanding of MPC (!!)

MPT3 toolbox knowledge is a plus (!)

Mathematical skills are a plus (!)

Professor(s)

Giancarlo Ferrari Trecate, Riccardo Cescon

Administration

Barbara Marie-Louise Frédérique Schenkel

Description:

Deep neural network (DNN) controllers have demonstrated remarkable success in controlling complex and nonlinear systems. These controllers are trained using data, eliminating the need to accurately model the system, which is often challenging due to the system’s complexity and inherent uncertainties. However, the stability and performance of DNN controllers when deployed in real-world remain significant concerns. While DNN controllers perform well within their training datasets, their behavior in scenarios outside the training data is not fully understood.

This project aims to design and evaluate DNN controllers for heating systems that incorporate heat storage. The heat storage functions as a battery: it heats (charges) when electricity generation is high (e.g., from renewable sources) and releases heat (discharges) when the heating demand is higher. The primary goal is to reduce heating costs while meeting heating demands. Ensuring the stability and performance of the designed controllers is crucial for safe operation and cost efficiency in real-world applications. To achieve this, we will employ the controller design approach introduced in [1], which provides stability and performance guarantees.

Comment

Main tasks:

Understand the fundamentals and dynamics of heating systems, including the role of heat storage (see, e.g., [2]).

Tailor DNN controllers for heating systems with integrated heat storage.

Follow the methodology outlined in [1] to train DNN controllers that ensure stability.

Established performance guarantees for the developed controller as per the approach in [1].

References:

[1] Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause, & Giancarlo Ferrari-Trecate. (2024). A PAC-Bayesian Framework for Optimal Control with Stability Guarantees.

[2] Bünning, F., Warrington, J., Heer, P., Smith, R., & Lygeros, J. (2022). Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps. Control Engineering Practice, 122, 105101.

Professor(s)

Giancarlo Ferrari Trecate, Christophe Salzmann

Administration

Barbara Marie-Louise Frédérique Schenkel

Site

https://www.epfl.ch/labs/la/pi/

Description:

Deep neural network (DNN) controllers have demonstrated remarkable success in controlling complex and nonlinear systems. These controllers are trained using data, eliminating the need to accurately model the system, which is often challenging due to the system’s complexity and inherent uncertainties. However, the stability and performance of DNN controllers when deployed in real-world remain significant concerns. While DNN controllers perform well within their training datasets, their behavior in scenarios outside the training data is not fully understood.

This project aims to design and evaluate DNN controllers for heating systems that incorporate heat storage. The heat storage functions as a battery: it heats (charges) when electricity generation is high (e.g., from renewable sources) and releases heat (discharges) when the heating demand is higher. The primary goal is to reduce heating costs while meeting heating demands. Ensuring the stability and performance of the designed controllers is crucial for safe operation and cost efficiency in real-world applications. To achieve this, we will employ the controller design approach introduced in [1], which provides stability and performance guarantees.

Comment

Contacts:

[email protected]

[email protected]

Main tasks:

Understand the fundamentals and dynamics of heating systems, including the role of heat storage (see, e.g., [2]).

Tailor DNN controllers for heating systems with integrated heat storage.

Follow the methodology outlined in [1] to train DNN controllers that ensure stability.

Established performance guarantees for the developed controller as per the approach in [1].

References:

[1] Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause, & Giancarlo Ferrari-Trecate. (2024). A PAC-Bayesian Framework for Optimal Control with Stability Guarantees.

[2] Bünning, F., Warrington, J., Heer, P., Smith, R., & Lygeros, J. (2022). Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps. Control Engineering Practice, 122, 105101.

Professor(s)

Giancarlo Ferrari Trecate

Administration

Barbara Marie-Louise Frédérique Schenkel

Site

https://www.epfl.ch/labs/la/pi/

Motivation:

Although they might be optimal with respect to some mathematical objective, conventional controllers might not result in driving trajectories that are preferred or expected by human passengers. One approach for a different family of controllers is to train a Machine Learning model from human ‘expert’ demonstrations, such that the resulting controller seems to act similarly to a human driver.

Description:

In this project, we would like to learn a Neural Network controller from a large existing dataset of human-driven trajectories. This includes implementing a baseline Imitation Learning method first, and a more advanced method second. Arising challenges are typically related to the stability of the learnt controller and its behavior outside the training data distribution.

Ideally, after development in simulation, the project leads to the successful deployment of the controller on our ready-to-use real-world mini race car system.

Skills needed

– Strong background in Machine Learning and the relevant software tools (Python, PyTorch)

– Qualitative understanding of car dynamics or similar mobile robots

– Significant experience in coding projects

– Familiarity with ROS/ROS2 (Robot Operating System) is a plus

– Familiarity with C++ is a plus (for real-time deployment)

Comment

Directly contact [email protected] and [email protected] if interested.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Johannes Christian Karl Waibel

Administration

Barbara Marie-Louise Frédérique Schenkel

The goal of the project is to study an application of the Koopman operator based control approach. For autonomous systems, Koopman operator theory shows that a nonlinear system can be represented as a linear system in a higher dimensional (possibly infinite dimensional) space. Thus, by lifting a nonlinear system to a higher dimensional space this approach enables the use of tools from linear systems theory for studying nonlinear systems. By using the Koopman operator approach, our goal is to obtain a linear system representation in a high dimensional space and employ linear control design techniques to control the actually nonlinear system.

The lifted system representation will be obtained in a data-driven fashion. As a result of this as well as further structural restrictions, the lifted representation never exactly captures the original system dynamics. Thus, the project will also aim for bounding these errors such that guarantees for the true closed-loop system can be provided by using robust control tools.

For now a pendulum system is considered as the application.

Skills needed:

-Advanced control systems (familiarity with frequency domain approach and robust control)

-Matlab/Simulink, LabVIEW

Comment

Contact: [email protected]

Professor(s)

Alireza Karimi

Administration

Mert Eyuboglu

Site

ddmac.epfl.ch, la.epfl.ch

Motivation: Shared decision-making is at the heart of any multi-agent system. Each agent independently aims to maximize its reward which depends on the decisions of all agents. The outcome of

such independent decision-making, however, is often inefficient in terms of social welfare and may violate other central objectives such as fairness. In traffic routing, for example, each driver aims to minimize its travel time independently which can result in traffic jams. Traffic routing under a central authority, however, can lead to a reduction in the aggregate travel time of all drivers. The field of mechanism design is concerned with how a central authority can incentivize independent decision-making towards an outcome that is aligned with the social welfare, possibly by incurring a cost on each agent. A key challenge is that the social welfare function can be complex and taking into account several possibly conflicting objectives, and may thus be a priori unknown. To address this, this master project aims to incorporate feedback from the central authority to learn a social welfare function that is aligned with the central authority’s preferences.

Comment

Requirements: We seek for motivated students with a strong mathematical, or computer science background. We do have some concrete ideas on how to tackle the above challenges, but we are always open for different suggestions. If you are interested, please send an email containing 1. one paragraph on your background and fit for the project, 2. your BS and MS transcripts to [email protected] and [email protected].

Professor(s)

Maryam Kamgarpour (Recherche en systèmes), Anna Maria Maddux

Administration

Sandra de Best

Site

https://www.epfl.ch/labs/sycamore/student-projects/

1. Overview

This project focuses on developing trajectory planning solutions for autonomous systems in interactive environments. The goal is to create algorithms enabling each robot to safely reach its destination while navigating complex scenarios, such as multiple autonomous cars at non-signalized intersections or multi-robot systems requiring formation control and coordination. Key challenges include collision avoidance with other agents and obstacles. The methodologies will be implemented and tested on an Nvidia Jetbot multi-robot testbed.

2. Responsibilities

– Literature review of existing trajectory planning methods within the frameworks of stochastic optimal control and game theory.

– Implementing the chosen control algorithms on the robotic testbed.

– Evaluating the algorithms for optimality, safety, and computational efficiency.

– Developing new algorithmic approaches for multi-robot trajectory planning based on the needs of the problem.

3. Requirements

– Familiarity with ROS/ROS2 and independence in trouble-shooting.

– Proficiency in Python for algorithm development and system integration.

– The project requires strong academic performance in control courses including model predictive control (MPC).

Comment

Note: To apply, send an email to [email protected] including 1. one paragraph on your background and fit for the project, 2. your BS and MS transcripts. This project will be supervised by Prof. Maryam Kamgarpour and Kai Ren ([email protected]).

Professor(s)

Maryam Kamgarpour (Recherche en systèmes), Kai Ren

Administration

Sandra de Best

Objective:

This project focuses on designing and synthesizing a state estimator for large and distributed systems, utilizing state-of-the-art optimization techniques. You will explore advanced control methods to develop a reliable estimator capable of efficiently managing the complexities of distributed systems, even in the presence of unknown and unforeseen disturbances. The approach will leverage recent advancements in robust control theory [1,2] and incorporate them into a state estimation framework, as demonstrated in [3]. Success in this project will require you to integrate knowledge from control theory, computer science, and optimization.

Project Overview:

State estimation is crucial in modern control systems, particularly in large and distributed networks where each sub-system must rely only on local and neighbours information. Your task will be to:

1. Conduct a Literature Review: Begin by surveying the existing body of knowledge on state estimation techniques and minimum regret control [1-3].

2. Design and Synthesis: Based on your literature review, design a state estimator tailored to a large, distributed system.

3. Simulation: Implement and test your state estimator using either Python or MATLAB. Simulate various scenarios to assess the performance of your design under different conditions.

Skills Required:

* Control Theory: A solid understanding of control systems and state estimation techniques is essential.

* Computer Science: Proficiency in programming, particularly in Python or MATLAB, is necessary for the simulation part of the project.

* Optimization: Knowledge of optimization methods will help in refining your state estimator for better performance and efficiency.

References:

[1]: Martin, Andrea, et al. “Safe control with minimal regret.” Learning for Dynamics and Control Conference. PMLR, 2022.

[2]: Martinelli, Daniele, et al. “Closing the gap to quadratic invariance: a regret minimization approach to optimal distributed control.” 2024 European Control Conference (ECC). IEEE, 2024.

[3]: Brouillon, Jean-Sébastien, Florian Dörfler, and Giancarlo Ferrari Trecate. “Minimal regret state estimation of time-varying systems.” IFAC-PapersOnLine 56.2 (2023): 2595-2600.

Comment

For more information, about the project, feel free to contact us by mail: [email protected]

For a better-formatted description of the project, please refer to the attached pdf.

Professor(s)

Giancarlo Ferrari Trecate, Daniele Martinelli

Administration

Barbara Marie-Louise Frédérique Schenkel

External

[email protected]

During the past few years, policy gradient methods have gained renewed attention within the control community, primarily due to their global convergence properties for certain classical control problems, such as $\mathcal{H}_2$ and $\mathcal{H}_\infty$ optimal control. However, the characteristics of policy gradient methods in the context of robust control with data-dependent uncertainty remain an open question.

This project focuses on the design and analysis of policy gradient algorithms for data-driven robust control. Given a set of noisy offline input-output data, and under assumptions on the boundedness of noise, the goal is to propose policy gradient algorithms minimizing worst-case closed-loop performance. Moreover, the properties of the proposed algorithms, such as global optimality and convergence rate, will be investigated.

Comment

Required knowledge:

1. Control theory

2. Convex optimization

3. Numerical analysis

References:

[1] Maryam Fazel, et al. “Global convergence of policy gradient methods for the linear quadratic regulator.” International conference on machine learning. PMLR, 2018.

[2] Kaiqing Zhang, Bin Hu, and Tamer Basar. “Policy Optimization for $\mathcal{H}_2$ Linear Control with $\mathcal{H}_\infty$ Robustness Guarantee: Implicit Regularization and Global Convergence.” Learning for Dynamics and Control. PMLR, 2020.

[3] Yang Zheng, Chih-fan Pai, and Yujie Tang. “Benign nonconvex landscapes in optimal and robust control, Part I: Global optimality.” arXiv preprint arXiv:2312.15332 (2023).

Professor(s)

Alireza Karimi, Zhaoming Qin

Administration

Barbara Marie-Louise Frédérique Schenkel

In this project, the objective is to deploy a swarm of mini-hovers (Crazyflies) using ROS and a hierarchical control scheme. The drones position would be then measured using a motion capture system (OptiTrack) which would be used for swarm controls. The student would need to: 1. Design a controller synthesis procedure for low-level controls of each drone. 2. Integrate the Crazyflie into the ROS architecture. 3. Design a high-level controller for swarm dynamics.

Comment

Assistant: Gupta Vaibhav

Professor(s)

Alireza Karimi, Christophe Salzmann

Administration

Barbara Marie-Louise Frédérique Schenkel

Site

ddmac.epfl.ch, la.epfl.ch

The aim of this semester project is to derive a dynamical model of the cooling system of a large business center. This system consists of several interconnected subsystems that interact through an extensive pipe network, and includes variable user demands as well as third-parties cooling components. These complexities make traditional modeling methods inadequate, requiring advanced approaches such as neural networks for accurate system identification. Interconnected neural networks allow to preserve the sparsity of the system, leading to the identification of each subsystem as well as the overall interconnected system. A precise model of each subsystem is fundamental for efficient monitoring and control, ultimately leading to energy savings and improved user comfort. Available measurements data from the real system will be used to train the designed interconnected neural network architecture.

Required knowledge and tools:

* Control theory

* Machine learning

* Simulink and Matlab

* Python and relevant libraries (PyTorch) for neural network implementation.

Reference papers:

[1] Terzi, E., Fagiano, L., Farina, M., & Scattolini, R. (2020). Structured modelling from data and optimal control of the cooling system of a large business center. Journal of Building Engineering, 28, 101043.

https://re.public.polimi.it/retrieve/0187b319-a62c-4570-b738-772c4d56d5f3/11311-1141455_Scattolini.pdf

[2] Terzi, E., Bonetti, T., Saccani, D., Farina, M., Fagiano, L., & Scattolini, R. (2020). Learning-based predictive control of the cooling system of a large business centre. Control Engineering Practice, 97, 104348. https://fagiano.faculty.polimi.it/docs/papers/BuildingMPC_ConEngPrac_2020.pdf

[3] Massai, L., Saccani, D., Furieri, L., & Ferrari-Trecate, G. (2023). Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators. arXiv preprint arXiv:2311.13967.

https://arxiv.org/pdf/2311.13967

[4] Saccani, D., & Bonetti, T. (2018). Modeling and learning-based hybrid predictive control with recurrent neural networks of the cooling station of a large business center.

Comment

Contact: [email protected]

Professor(s)

Giancarlo Ferrari Trecate, Laura Meroi

Administration

Barbara Marie-Louise Frédérique Schenkel