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Get Outlook for Androidhttps://aka.ms/AAb9ysg ________________________________ From: Australian Centre For Robotics Administration Sent: Tuesday, 21 July 2026 15:15:45 To: Australian Centre For Robotics Administration acfr.admin@sydney.edu.au; everybody everybody@acfr.usyd.edu.au; students@acfr.usyd.edu.au students@acfr.usyd.edu.au; seminars@acfr.usyd.edu.au seminars@acfr.usyd.edu.au; 'academics-meeting@acfr.usyd.edu.au' academics-meeting@acfr.usyd.edu.au Cc: Ian Manchester ian.manchester@sydney.edu.au Subject: ACFR Seminar Series: Asst. Professor Bowen Yi - Input–Output Data-Driven Control of Continuous-Time Systems When: Wednesday, July 22, 2026 1:00 PM-2:00 PM. Where: ACFR J04 Level 2 Seminar Area:; https://maps.app.goo.gl/PUJdbB9oUTtg5fLr8; ZOOM:; https://uni-sydney.zoom.us/s/87306457455

Faculty of Engineering

ACFR Seminar Series

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Input–Output Data-Driven Control of Continuous-Time Systems

Presented by Asst. Professor Bowen Yi Date: Wednesday, 22 July 2026, at 1:00 pm AEST Venue: ACFR seminar area, J04 level 2 (Rose St Buildinghttps://url.au.m.mimecastprotect.com/s/6O8_C1WLPxckoYOw1u4UkfVNAuX?domain=t.e2ma.net) Zoom ID: https://uni-sydney.zoom.us/s/87306457455https://url.au.m.mimecastprotect.com/s/JpqhC2xMQziRXw6ovixcxf5GEwn?domain=t.e2ma.net Abstract:

In this talk, we address data-driven output-feedback control of continuous-time linear systems using only measured input–output trajectories. The key idea is to employ Kreisselmeier’s adaptive filter to construct a canonical non-minimal realization of the unknown plant, thereby avoiding explicit system identification and full-state measurement. In the first part, we consider the stabilization of general MIMO linear systems from a finite input–output trajectory. By interpreting the adaptive filter as an observer for a stabilizable non-minimal realization, we formulate the controller synthesis problem as a data-dependent linear matrix inequality. A data-driven decomposition is introduced to separate the excited controllable component from the stable uncontrollable dynamics, thereby relaxing the required rank conditions. We also discuss conditions for informative data collection and a reduced-order filter implementation that improves computational scalability. In the second part, we briefly extend this framework to data-driven linear quadratic control. By augmenting the filter-generated realization, we develop a value-iteration method that learns an implementable output-feedback controller from input–output data while recovering the optimal state-feedback performance.

Bio:

Bowen Yi is an Assistant Professor in the Department of Electrical Engineering at Polytechnique Montréal, an engineering school affiliated with Université de Montréal, and a member of GERAD (Groupe d’études et de recherche en analyse des décisions). He received his Ph.D. degree from Shanghai Jiao Tong University, China, in 2019. From 2017 to 2019, he was a visiting researcher at the Laboratoire des Signaux et Systèmes (L2S), CNRS–CentraleSupélec, France.

He subsequently held postdoctoral research positions at the Australian Centre for Robotics at the University of Sydney (2019 – 2022) and the Robotics Institute at the University of Technology Sydney (2022 – 2023).

His research interests include nonlinear systems, particularly estimation, control, and learning, and their applications to robotics. He received the 2019 IEEE CCTA Best Student Paper Award and the Australian Research Council Discovery Early Career Researcher Award (ARC DECRA) in 2024.

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