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SUMMARY:Real-time Optimization as a Cyber-Physical System
DTSTART:20210216T090000
DTEND:20210216T100000
DTSTAMP:20260502T060631Z
UID:c9d393f01f9434f776ba125b4bbcb3e2c52e8e3a67bf8d545dd5ee68
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Dominic Liao-Mc Pherson\, Automatic Control Laboratory\, E
 TH Zürich\nAbstract: Accelerating advances in computing\, sensors\, and d
 ata generation are beginning to profoundly impact society through the cont
 rol and automation of complex systems such as electrical grids (Smart Grid
 )\, manufacturing processes (Industry 4.0)\, and the increasing ubiquity o
 f drones\, self-driving cars\, and other autonomous systems. Due to their 
 complexity\, these intelligent systems are controlled by algorithms runnin
 g in real-time and in closed-loop with the system\, rather than closed-for
 m feedback laws\, resulting in a tightly coupled cyber-physical systems (C
 PS). Developing reliable real-time algorithms and the theoretical tools ne
 eded to understand the complex feedback interactions between algorithms an
 d the physical systems they control is challenging but essential for reali
 zing the potential of ubiquitous closed-loop control and for building the 
 public trust necessary for widespread deployment.\n\nIn this talk\, I argu
 e for a cyber-physical systems view of optimization/algorithm based contro
 l wherein the physical system and the algorithms controlling it are design
 ed and analyzed together. First\, I present Time-distributed Optimization 
 (TDO)\, a unifying framework for studying the system theoretic consequence
 s of computational limits in the context of Model Predictive Control (MPC)
 . I show that it is possible to recover the stability and robustness prope
 rties of optimal MPC despite limited computational resources and illustrat
 e how this CPS viewpoint can be exploited to certify the closed-loop syste
 m. Further\, I illustrate the applicability of these methods in the real-w
 orld through diesel engine\, and autonomous driving examples. Second\, I d
 iscuss ongoing work on feedback equilibrium seeking wherein the goal is to
  regulate a physical system to the solution of a time-varying variational 
 inequality. I present tracking error\, stability\, and robustness results 
 and provide motivating examples in smart buildings and DC power grids. Fin
 ally\, I briefly discuss how a CPS viewpoint can be used for certification
 \, health monitoring\, and supervision of algorithmic controllers and prov
 ide future perspectives on extensions to equilibrium seeking and learning 
 algorithms.\n\nBio: Dominic Liao-McPherson obtained his BASc (with High Ho
 nours) in Engineering Science (Aerospace Option) from the University of To
 ronto in 2015 and his PhD in Aerospace Engineering and Scientific Computin
 g from the University of Michigan in 2020. His research interests lie at t
 he interface of systems theory\, optimization\, variational analysis\, and
  numerical methods with applications in autonomous vehicles\, smart grids\
 , and advanced manufacturing. He is a recipient of the Prof. Pierre T. Kab
 amba award\, the Richard and Eleanor Towner prize\, the Sir James Lougheed
  award\, and was a finalist in the 2019 ECC best student paper competition
 .
LOCATION:https://epfl.zoom.us/j/88475072848?pwd=RG4rTFdFV0NKYTMyL2JBM2FJRy
 tIQT09
STATUS:CONFIRMED
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