Mingzhou YIN 殷明周
Doctoral Researcher,
Automatic Control Laboratory,
Dept. Information Technology & Electrical Engineering,
ETH Zürich
Email: myin@control.ee.ethz.ch
ETL K 14.1, Physikstrasse 3
8092 Zürich, Switzerland
Biography
Mingzhou Yin is a doctoral student supervised by Prof. Roy S. Smith in the Automatic Control Laboratory at ETH Zurich since February 2019. He has been supported by the project “Modeling, Identification and Control of Periodic Systems in Energy Applications” from the Swiss National Science Foundation. He received his MSc degree cum laude in control & simulation at the Faculty of Aerospace Engineering, Delft University of Technology, the Netherlands in 2018. His master's thesis research is on envelope-protected non-linear control of over-actuated aircraft in collaboration with Lockheed Martin. He received the joint bachelor’s degree in Mechanical Engineering at Shanghai Jiao Tong University, China and the University of Hong Kong, China with first-class honours in 2016. He was the recipient of the IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award and the Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize in 2023.
Research area
His research interests include data-driven modeling, simulation & control, sparse learning theory, system identification with subspace and regularized methods, model predictive control, and periodic system theory.
Updates
December 2023: A preprint where sum-of-rational optimization is applied to the classical rational transfer function identification problem.
December 2023: I presented at CDC 2023 on error bounds for kernel-based system identifcation.
December 2023: A preprint where we discussed the issues of regularization, estimation, and constraint tightening in stochastic data-driven predictive control.
November 2023: A preprint where we proposed a new closed-loop identification framework that is dual to the input-output parameterization for control synthesis.
October 2023: Our work on application of stochastic indirect data-driven predictive control to building control has been published in Applied Energy with gold open access.
September 2023: I gave a contributed talk at ERNSI 2023 on error bounds for kernel-based system identifcation.
September 2023: I was invited for a talk at the Institute of Automatic Control (IRT), Leibniz University Hannover on stochastic data-driven control.
July 2023: I am deeply honored to share the IEEE Control Systems Society Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize 2023 with my coauthor Defne Ege Ozan.
July 2023: Our papers on closed-loop identification and kernel-based system identification have been accepted for presentation at the 2023 62nd IEEE Conference on Decision and Control (CDC).
June 2023: Our paper on reliable error bounds for kernel-based system identification with unknown hyperparameters has been accepted for publication in IEEE Control Systems Letters.
June 2023: I am deeply honored to receive the IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award 2023.
March 2023: A preprint where we proposed a new closed-loop identification framework that is dual to the system level parameterization for control synthesis.
March 2023: An IfA coffee talk where I discussed a few lesser-known aspects of \(l_1\)-norm regularization.
Extra
He takes photos. Gallery