BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IEEE - UK and Ireland Section - ECPv5.16.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ieee-ukandireland.org
X-WR-CALDESC:Events for IEEE - UK and Ireland Section
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20211031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20210520T130000
DTEND;TZID=Europe/London:20210520T140000
DTSTAMP:20221203T231738
CREATED:20210516T091516Z
LAST-MODIFIED:20210516T091516Z
UID:22145-1621515600-1621519200@www.ieee-ukandireland.org
SUMMARY:Technical Webinar | Risk-averse Model Predictive Control: Theory and Algorithms
DESCRIPTION:Technical Webinar | Risk-averse Model Predictive Control: Theory and Algorithms by Dr Pantelis SopasakisAbstract \n \nWhen it comes to modelling uncertain systems\, there exist two prevailing approaches; namely\, to determine (bounded) sets that contain all possible realisations of the uncertain disturbances or parameters\, and to consider a certain probability distribution of the uncertainty. The former approach disregards any statistical or probabilistic information that is usually available and leads to overly conservative worst-case formulations\, while the latter relies on the assumption that the underlying probability distributions are exactly known. In practice the characteristics of such uncertain disturbances are estimated from data\, therefore one needs to take into account this uncertain uncertainty in order to design performant and reliable control and estimation systems. \nRisk-averse model predictive control (MPC) is an optimisation-based control methodology that accounts for the inexact knowledge of the involved probability distributions using the theory of risk measures and unifies the worst-case and stochastic formulations of MPC. In fact\, the risk-averse approach allows to interpolate between the worst-case (minimax) and expectation-based flavours of MPC and calls for new notions of (risk-based) stability that generalise robust and mean square stability. In light of the inexact knowledge of the probability distributions\, probabilistic (aka chance) state constraints become what is known as ambiguous chance constraints and can be approximated by risk-based constraints. In multistage optimal control formulations\, one needs to take into account the propagation of ambiguity in time which leads to multistage ambiguous chance constraints. \nRisk-averse MPC problems can lead to exceptionally large-scale problems where the cost function is expressed as the composition of several non-smooth operators. However\, risk-averse problems possess a certain structure that can be exploited to devise massively parallelisable (proximal) numerical optimisation algorithms that can run on graphics processing units (GPUs) and allow us to solve them fast and accurately. \n\nAbout the speaker \nDr Pantelis Sopasakis was born in Athens\, Greece\, in 1985. He received a diploma (M.Eng.) in chemical engineering in 2007 and an M.Sc. with honours in applied mathematics in 2009 from the National Technical University of Athens. \nIn 2012\, he defended his PhD thesis titled “Modelling and control of biological and physiological systems” at the School of Chemical Engineering\, NTU Athens. He has held postdoctoral positions at IMT Lucca\, KU Leuven and University of Cyprus. Since 2019 he has been a lecturer at the School of Electronics\, Electrical Engineering and Computer Science (EEECS) and the i-AMS research centre at Queen’s University Belfast. \nHis current research interests revolve around model predictive control for uncertain systems and numerical optimisation methods and algorithms for large-scale stochastic optimal control problems \n\nRegister Now
URL:https://www.ieee-ukandireland.org/event/technical-webinar-risk-averse-model-predictive-control-theory-and-algorithms/
CATEGORIES:Control and Communication Ireland
ORGANIZER;CN="Dr%20Nikolaos%20Athanasopoulos":MAILTO:n.athanasopoulos@qub.ac.uk
END:VEVENT
END:VCALENDAR