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SUMMARY:Data as models? A closer look at data-driven control systems - Roy
  Smith\, ETH Zurich
DTSTART:20251016T130000Z
DTEND:20251016T140000Z
UID:TALK239122@talks.cam.ac.uk
CONTACT:Fulvio Forni
DESCRIPTION:The resurgence of data-driven dynamic models offers the tantal
 ising prospect of being able to implement feedback controllers directly fr
 om measurements of the trajectories of the system to be controlled. Data-e
 nabled predictive control (DeePC)\, data-driven predictive control (DDPC)\
 , and similar variants circumvent the traditional approach of identifying 
 a dynamic model as an intermediate step in the control design process. Suc
 h approaches require regularisation to trade off between the estimation an
 d control objectives. Another weakness is the inability to effectively han
 dle unmeasured disturbances. We take a somewhat different view here that t
 he data matrices used for data-driven control are themselves models (signa
 l matrix models) that use the system trajectories as the representation. W
 e will use this approach to construct Kalman filters and predictive contro
 llers. Regularisation is no longer necessary and unmeasured disturbances c
 an be effectively controlled.\n\nThe seminar will be held in JDB Seminar R
 oom\, Department of Engineering
LOCATION:JDB Seminar Room\, Department of Engineering
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