BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240215T185344Z UID:4DADA276-BFFC-4160-BE26-8E2C9A09DE5F DTSTART;TZID=America/Los_Angeles:20240214T173000 DTEND;TZID=America/Los_Angeles:20240214T190000 DESCRIPTION:Intelligent systems are becoming increasingly prevalent in our society\, e.g.\, self-driving cars are being developed on an industrial sc ale and smart grids are at the forefront of efforts to combat climate chan ge. Model predictive control (MPC)\, a powerful optimization-based constra ined control technique\, is a key enabling technology for the next generat ion of intelligent systems. Often the most significant challenge when depl oying model predictive controllers is solving complex trajectory optimizat ion problems in real-time. It is often not possible to solve these problem s to full optimality in practice due to computational limits\, instead we resort to computationally cheaper suboptimal predictive control strategies . However\, this potentially sacrifices some of MPC's stability and robust ness guarantees. In this talk\, I present Time-distributed Optimization (T DO)\, a unifying framework for studying the system theoretic consequences of computational limits in the context of Model Predictive Control (MPC). By framing suboptimal MPC as a feedback interconnection between the physic al plant and an optimization algorithm\, I derive sufficient conditions fo r stability and robustness of model predictive controllers under computing power and/or communication limits. Further\, I illustrate the applicabili ty of the these methods in the real-world through diesel engine\, and auto nomous driving examples.\n\nSpeaker(s): Dominic Liao-McPherson \n\nAgenda: \nThe event takes place on Wednesday Feb 14th from 5:30pm to 7:00pm.\n\n5 :30pm Start\n\n- Gathering\n\n- Introduction\n\n5:40pm\n\n- Talk\n\n- Disc ussion\n\n7:00PM End\n\nSpeaker: Dominic\n\nModerator: Dejan\n\nUniversity of British Columbia \, Vancouver \, British Columbia\, Canada\, Virtual: https://events.vtools.ieee.org/m/401418 LOCATION:University of British Columbia \, Vancouver \, British Columbia\, Canada\, Virtual: https://events.vtools.ieee.org/m/401418 ORGANIZER:kihas@ieee.org SEQUENCE:62 SUMMARY:A Systems-theoretic Viewpoint on Real-time Optimization for Model P redictive Control URL;VALUE=URI:https://events.vtools.ieee.org/m/401418 X-ALT-DESC:Description: <br /><p>Intelligent systems are becoming increasin gly prevalent in our society\, e.g.\, self-driving cars are being develope d on an industrial scale and smart grids are at the forefront of efforts t o combat climate change. Model predictive control (MPC)\, a powerful optim ization-based constrained control technique\, is a key enabling technology for the next generation of intelligent systems. Often the most significan t challenge when deploying model predictive controllers is solving complex trajectory optimization problems in real-time. It is often not possible t o solve these problems to full optimality in practice due to computational limits\, instead we resort to computationally cheaper suboptimal predicti ve control strategies. However\, this potentially sacrifices some of MPC's stability and robustness guarantees. In this talk\, I present Time-distri buted Optimization (TDO)\, a unifying framework for studying the system th eoretic consequences of computational limits in the context of Model Predi ctive Control (MPC). By framing suboptimal MPC as a feedback interconnecti on between the physical plant and an optimization algorithm\, I derive suf ficient conditions for stability and robustness of model predictive contro llers under computing power and/or communication limits. Further\, I illus trate the applicability of the these methods in the real-world through die sel engine\, and autonomous driving examples.&nbsp\;</p><br /><br />Agenda : <br /><p>The event takes place on Wednesday Feb 14th from 5:30pm to 7:00 pm.&nbsp\;</p>\n<p>5:30pm Start&nbsp\;</p>\n<p>&nbsp\;- Gathering</p>\n<p> &nbsp\;- Introduction&nbsp\;</p>\n<p>5:40pm</p>\n<p>&nbsp\;- Talk&nbsp\;</ p>\n<p>&nbsp\;- Discussion</p>\n<p>7:00PM End&nbsp\;</p>\n<p>&nbsp\;</p>\n <p>Speaker: Dominic</p>\n<p>Moderator: Dejan&nbsp\;</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR