Andrea Martinelli

Academic bio

I am a PhD student with the Automatic Control Laboratory at ETH Zurich, Switzerland, supervised by Prof. J. Lygeros.

I obtained the BSc degree in Industrial 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 Automatic Control Laboratory of EPFL, 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:

• Data-driven control, learning-based optimization of dynamical systems, approximate dynamic programming

• Control of interconnected systems with focus on renewable energy distribution models.

News

• Oct 2021: Our paper on data-driven optimal control with a relaxed linear program has been accepted for publication in Automatica!

• Oct 2021: A new preprint is available on parallel and flexible dynamic programming via the randomized mini-batch operator

• Sep 2021: Check out our preprint on the synthesis of Bellman inequalities for data-driven optimal control

• Jul 2021: We have an OPEN POSITION taken! for a SA/MA project on plug-and-play control of interconnected systems

• Jul 2021: Our work on Data-driven control with artificial constraints has been accepted at the IEEE Conference on Decision and Control 2021 (Austin, Texas)

• Jun 2021: We will attend the learning for dynamics & control (L4DC) conference in Zurich, Switzerland

• May 2021: We presented our work on passivity-based control for large-scale systems at the 2021 American Control Conference (New Orleans, Louisiana)

• Feb 2021: Check out the summer school on mathematical foundations of data-driven control jointly organized by ETH & EPFL!

• Dec 2020: We have an OPEN POSITION taken! for a SA/MA Project on dynamic programming via the randomized mini-batch Gauss-Seidel operator.

• Dec 2020: Our paper on passivity-based decentralized control for large-scale systems has been accepted for publication in IEEE Control Systems Letters.

• Dec 2020: We have an OPEN POSITION taken! for a SA/MA Project on data-driven optimal control via linear programming.

• Dec 2020: Check out our new preprint on data-driven optimal control with a relaxed linear program.