BEGIN:VCALENDAR METHOD:REQUEST PRODID:Microsoft Exchange Server 2010 VERSION:2.0 BEGIN:VTIMEZONE TZID:AUS Eastern Standard Time BEGIN:STANDARD DTSTART:16010101T030000 TZOFFSETFROM:+1100 TZOFFSETTO:+1000 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=1SU;BYMONTH=4 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010101T020000 TZOFFSETFROM:+1000 TZOFFSETTO:+1100 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=1SU;BYMONTH=10 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ORGANIZER;CN=Australian Centre For Robotics Administration;SENT-BY="mailto:s abrina.asri@sydney.edu.au":mailto:acfr.admin@sydney.edu.au ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=everybody: mailto:everybody@acfr.usyd.edu.au ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=students@a cfr.usyd.edu.au:mailto:students@acfr.usyd.edu.au ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=seminars@a cfr.usyd.edu.au:mailto:seminars@acfr.usyd.edu.au ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='academics -meeting@acfr.usyd.edu.au':mailto:academics-meeting@acfr.usyd.edu.au ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Ian Manche ster:mailto:ian.manchester@sydney.edu.au DESCRIPTION;LANGUAGE=en-US:Faculty of Engineering\n\n\nACFR Seminar Series\ n[https://d31hzlhk6di2h5.cloudfront.net/20260721/44/a0/ac/85/83de99ae1318d a0add393c81.png]\n[https://d31hzlhk6di2h5.cloudfront.net/20260721/90/62/a1 /a0/e806283be685b4d065bc82ba.jpg]\nInput–Output Data-Driven Control of C ontinuous-Time Systems\n\nPresented by Asst. Professor Bowen Yi\nDate: Wed nesday\, 22 July 2026\, at 1:00 pm AEST\nVenue: ACFR seminar area\, J04 le vel 2 (Rose St Building)\nZoom ID: https://uni-sydney.zoom.us/ s/87306457455\nAbstract:\n\n\nIn this talk\, we address data-d riven output-feedback control of continuous-time linear systems using only measured input–output trajectories. The key idea is to employ Kreisselm eier’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. B y interpreting the adaptive filter as an observer for a stabilizable non-m inimal realization\, we formulate the controller synthesis problem as a da ta-dependent linear matrix inequality. A data-driven decomposition is intr oduced to separate the excited controllable component from the stable unco ntrollable dynamics\, thereby relaxing the required rank conditions. We al so discuss conditions for informative data collection and a reduced-order filter implementation that improves computational scalability. In the seco nd part\, we briefly extend this framework to data-driven linear quadratic control. By augmenting the filter-generated realization\, we develop a va lue-iteration method that learns an implementable output-feedback controll er from input–output data while recovering the optimal state-feedback pe rformance.\n\nBio:\n\n\nBowen Yi is an Assistant Professor in the Departme nt of Electrical Engineering at Polytechnique Montréal\, an engineering s chool affiliated with Université de Montréal\, and a member of GERAD (Gr oupe d’études et de recherche en analyse des décisions). He received h is Ph.D. degree from Shanghai Jiao Tong University\, China\, in 2019. From 2017 to 2019\, he was a visiting researcher at the Laboratoire des Signau x et Systèmes (L2S)\, CNRS–CentraleSupélec\, France.\n\nHe subsequentl y held postdoctoral research positions at the Australian Centre for Roboti cs at the University of Sydney (2019 – 2022) and the Robotics Institute at the University of Technology Sydney (2022 – 2023).\n\nHis research in terests include nonlinear systems\, particularly estimation\, control\, an d learning\, and their applications to robotics. He received the 2019 IEEE CCTA Best Student Paper Award and the Australian Research Council Discove ry Early Career Researcher Award (ARC DECRA) in 2024.\n\n[https://d31hzlhk 6di2h5.cloudfront.net/20260721/2e/8d/c7/1c/1c70baef8b350e3840963efe.png]\n \n[Web Site]\n[LinkedIn]\n[X]\n[YouTub e]\n\nCopyright © 2026\n\nThe University of Sydney\n\nPlease add acfr.admin@sydney.edu.au to your addr ess book or senders safe list to make sure you continue to see our emails in the future.\n\n\n\nCricos Code 00026A TEQSA PRV12057\nDisclaimer | Privacy statement |\n\n\n UID:040000008200E00074C5B7101A82E0080000000012F24EF8CF18DD01000000000000000 0100000009CACDC11BFAD4C45AD5B5F1C0BE1C2D7 SUMMARY;LANGUAGE=en-US:ACFR Seminar Series: Asst. 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