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SUMMARY:Probabilistically Robust Decision Making for Uncertain Dynamical S
 ystems - Venkatraman Renganathan\, Cranfield University
DTSTART:20250612T130000Z
DTEND:20250612T140000Z
UID:TALK233224@talks.cam.ac.uk
CONTACT:Fulvio Forni
DESCRIPTION:Typically\, how much do we know about an uncertain dynamical s
 ystem (UDS) matters a lot when we want to control them. Aiming to accurate
 ly capture the evolution of such UDS is impossible as true system uncertai
 nties cannot be captured exactly. Lack of exact system knowledge increases
  the difficulty in estimating the limits of the uncertain system’s perfo
 rmance. As a result\, we often seek to control such UDS such that the resu
 lting control decisions from Robust Decision Making (RDM) paradigms render
  the UDS insensitive to what we don’t know about them. However\, nature 
 can violate the assumptions that the RDM module assume for the system unce
 rtainties with small probability. Controlling UDS under such unforeseen ev
 ents necessitate the addition of probabilistic rigour on top of the existi
 ng RDM approaches. In this talk\, I shall propose a Probabilistic RDM (PRD
 M) approach using the uncertain gap between the dynamical system models (w
 ith and without the uncertainty) induced by appropriate distance metric. T
 he proposed framework will allow us to analyse the potential performance d
 egradation of a control action on an UDS when such rare violation events o
 ccur. The fertile nature of the probabilistic robust control research area
  will be highlighted using a list of interesting future research direction
 s.\n\nThe seminar will be held in JDB Seminar Room\, Department of Enginee
 ring\, and online (zoom): https://newnham.zoom.us/j/92544958528?pwd=YS9PcG
 RnbXBOcStBdStNb3E0SHN1UT09
LOCATION:JDB Seminar Room\, Department of Engineering and online (Zoom)
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