Andrea Martinelli
PhD Student
Automatic Control Laboratory
Swiss Federal Institute of Technology (ETH) Zurich
Academic bio
I am a PhD student with the Automatic Control Laboratory (IfA) at ETH Zurich, Switzerland, supervised by Prof. J. Lygeros.
I obtained the BSc degree in Management Engineering in 2015 and the MSc degree in Control Engineering in 2017, both from Politecnico di Milano, Italy. In 2017, I was a visiting student at the Laboratoire d'Automatique (LA) of EPF Lausanne, Switzerland, where I developed my MSc thesis under the guidance of Prof. G. Ferrari-Trecate. In 2018, I worked as research assistant with Prof. R. Scattolini at the Systems & Control group at DEIB, Politecnico di Milano, Italy.
Research interests
I am interested in investigating theoretical and practical aspects of control theory, including:
Optimal control, reinforcement learning and approximate dynamic programming
Networked dynamical systems with a focus on renewable energy distribution models.
News 2022
Nov: On Nov 23 we will host our usual IfA Open House, where students can get to know us and our research! Some more info here
Nov: I am giving a talk at ETH Zurich on the linear programming approach, you can check out the slides here
Sep: We will present our work on data-driven control of affine systems at the CDC 2022 in Cancun, Mexico
Sep: Start of the fall semester at ETH: I am head TA of Linear System Theory
Jul: Our paper on plug-and-play distributed MPC has been accepted for presentation at the 2022 Conference on Decision and Control (CDC)
Jul: We are hosting the IFAC Conference on Networked Systems (NecSys) here at IfA/ETH Zurich
June: Our work on data-driven control of affine systems is now published in IEEE Control Systems Letters
May: Our paper was accepted for publication at the International Conference on Machine Learning (ICML)
Mar: Check out our new preprint on data-driven control of affine systems
Feb: Spring semester starts: I am a TA in the Advanced topics in control course. Check out the teaser video by Prof. Dorfler
Jan: We released a new preprint on probabilistic variance-reduced policy gradient methods
Older News
2021
Dec: We are presenting our recent publication on data-driven control at the CDC 2021, Austin, Texas (USA)
Oct: Our paper on data-driven optimal control with a relaxed linear program has been accepted for publication in Automatica!
Oct: A new preprint is available on parallel and flexible dynamic programming via the randomized mini-batch operator
Sep: Check out our preprint on the synthesis of Bellman inequalities for data-driven optimal control
Jul: We have an
OPEN POSITIONtaken! for a SA/MA project on plug-and-play control of interconnected systemsJul: Our work on Data-driven control with artificial constraints has been accepted at the IEEE Conference on Decision and Control 2021 (Austin, Texas)
Jun: We will attend the learning for dynamics & control (L4DC) conference in Zurich, Switzerland
May: We presented our work on passivity-based control for large-scale systems at the 2021 American Control Conference (New Orleans, Louisiana)
Feb: Check out the summer school on mathematical foundations of data-driven control jointly organized by ETH & EPFL!
2020
Dec: We have an
OPEN POSITIONtaken! for a SA/MA Project on dynamic programming via the randomized mini-batch Gauss-Seidel operator.Dec: Our paper on passivity-based decentralized control for large-scale systems has been accepted for publication in IEEE Control Systems Letters.
Dec: We have an
OPEN POSITIONtaken! for a SA/MA Project on data-driven optimal control via linear programming.Dec: Check out our new preprint on data-driven optimal control with a relaxed linear program.
