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X-WR-CALNAME: Data-driven Learning and Control Seminar: Miroslav Krstic (UC
 SD) 
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260520T184740Z
UID:tag:localist.com\,2008:EventInstance_52135596769241
DTSTART:20260326T160000Z
DTEND:20260326T170000Z
DESCRIPTION:Data Driven Learning and Control seminar series is organized by
  the Information and Decision Science Lab at Cornell University and aims t
 o explore the latest advancements and interdisciplinary approaches to data
 -driven learning and control systems.\n\nWatch on YouTube Live\n \n\nOptim
 al Control or Optimal Learning?… How About Both!\n\nReal-time learning s
 trategies where\, at infinite time and under PE conditions\, gains become 
 optimal are common — they are a particular variety of RL and called also
  adaptive dynamic programming or "adaptive-optimal" control. What is\, in 
 contrast\, truly of interest to the practitioner are OPTIMAL-ADAPTIVE cont
 rollers (note the commuted order of the two adjectives). They are optimal 
 during the entire infinite horizon. Conceived in 1997 by the speaker\, thi
 s idea failed to spread widely because in general its controllers - while 
 given explicitly - have complicated expressions. The exception\, where the
  optimal-adaptive controllers become elegant\, are the so-called “driftl
 ess systems\,” of the form dx/dt = g(x)u (with f=0)\, where dim(x)>dim(u
 ). Nonholonomic mobile robots are archetypal driftless systems. For such v
 ehicles\, we first present globally uniformly Lagrange asymptotically stab
 ilizing (GULAS) feedback laws\, which don’t contradict Brockett’s cond
 ition. With their strict CLFs\, adaptive LgV-type feedback laws are then d
 esigned for vehicles with completely unknown wheel traction coefficients. 
 These controllers optimize cost functionals that not only penalize the lon
 gitudinal and angular velocity inputs\, as well as the unicycle’s three 
 configuration states\, but also the parameter estimation errors - over the
  entire infinite time horizon (not only in the asymptotic limit). As a bon
 us\, control designs that complete parking in user-desired time will be sh
 own: (1) a time-varying feedback\, with gains that are singular at termina
 l time but keep the controls bounded\, and (2) a static homogeneous feedba
 ck\, which is nonsmooth at the target values of position and heading.\n \n
 \nBio: Miroslav Krstic is Distinguished Professor of mechanical and aerosp
 ace engineering\, holds the Alspach endowed chair\, and is the founding di
 rector of the Center for Control Systems and Dynamics at UC San Diego. He 
 also serves as Senior Associate Vice Chancellor for Research at UCSD.\n\nA
 s a graduate student\, Krstic won the UC Santa Barbara best dissertation a
 ward and student best paper awards at CDC and ACC. Krstic has been elected
  Fellow of IEEE\, IFAC\, ASME\, SIAM\, AAAS\, IET (UK)\, and AIAA (Assoc. 
 Fellow) - and as a foreign member of the Serbian Academy of Sciences and A
 rts and of the Academy of Engineering of Serbia. He has received the IEEE 
 Roger W. Brockett Control Systems Award\, SIAM Reid Prize\, ASME Oldenburg
 er Medal\, Control Systems Society Distinguished Member Award\, the PECASE
 \, NSF Career\, and ONR Young Investigator awards. Krstic is a Fellow-Amba
 ssador of the French CNRS and has also been awarded the Springer Visiting 
 Professorship at UC Berkeley\, the Distinguished Visiting Fellowship of th
 e Royal Academy of Engineering\, the Invitation Fellowship of the Japan So
 ciety for the Promotion of Science\, and four honorary professorships outs
 ide of the United States.\n\nHe serves as editor-in-chief of Systems & Con
 trol Letters and has been serving as senior editor in Automatica and IEEE 
 Transactions on Automatic Control\, as editor of two Springer book series\
 , and has served as Vice President for Technical Activities of the IEEE Co
 ntrol Systems Society and as chair of the IEEE CSS Fellow Committee. Krsti
 c has coauthored 19 books on adaptive\, nonlinear\, and stochastic control
 \, extremum seeking\, control of PDE systems including turbulent flows\, a
 nd control of delay systems.
LOCATION:
SUMMARY: Data-driven Learning and Control Seminar: Miroslav Krstic (UCSD) 
URL;VALUE=URI:https://events.cornell.edu/event/data-driven-learning-and-con
 trol-seminar-miroslav-krstic-ucsd
CATEGORIES:Seminar
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