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SUMMARY:Data-driven control of hybrid systems and Chance-Constrained optim
 ization - Raphael Jungers\, UC Louvain
DTSTART:20230214T140000Z
DTEND:20230214T150000Z
UID:TALK197233@talks.cam.ac.uk
CONTACT:Fulvio Forni
DESCRIPTION:Control systems are increasingly complex\, often at the point 
 that obtaining a model for them is out of reach. In some situations\, (par
 ts of) the systems are proprietary\, so that the very equations that rule 
 their behaviour cannot be known. On the other hand\, the ever-growing prog
 ress in hardware technologies often enables one to retrieve massive data\,
  e.g. from embedded sensors. Because of these evolutions\, control theory 
 is moving from a model-based towards a model-free and data-driven paradigm
 .\n\nFor Linear Time-invariant systems\, classical results from Identifica
 tion theory provide a rather straightforward approach. However\, these app
 roaches become useless (or at least inefficient) if one relaxes the strong
  assumptions they rely upon (linearity\, gaussian noise\, etc.). This is e
 specially the case in safety-critical applications\, where one needs guara
 ntees on the performance of the obtained solution.\n\nDespite these diffic
 ulties\, one may sometimes recover firm guarantees on the behaviour of the
  system. This may require to change one’s point of view on the nature of
  the guarantees we ask.  I will provide examples of such results for diffe
 rent control tasks and different complex systems\, and will raise the ques
 tion of theoretical fundamental barriers for these problems.\n\nThe semina
 r will be held in the Control Lab BN4-85\, Department of Engineering\, and
  online (zoom): https://us06web.zoom.us/j/87986687566?pwd=MGJScmMwd2lwT0tV
 MHNmWmxSa05XZz09
LOCATION:Department of Engineering / Online (Zoom)
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