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DTSTART:20240310T030000
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DTSTAMP:20250208T173154Z
UID:E10DF889-34DA-43B0-B0AE-CFCD5E08B55C
DTSTART;TZID=America/New_York:20240424T140000
DTEND;TZID=America/New_York:20240424T150000
DESCRIPTION:With an increasing number of multirotor unmanned aerial vehicle
 s (UAVs)\, solutions supporting the improvement in their precision of oper
 ation and safety of autonomous flights are gaining importance. They are pa
 rticularly crucial in transportation tasks\, where control systems are req
 uired to provide a stable and controllable flight in various environmental
  conditions\, especially after changing the total mass of the UAV (by addi
 ng extra load). In this presentation\, the problem of using only available
  basic sensory information for fast\, locally best\, iterative real-time a
 uto-tuning of parameters of fixed-gain altitude controllers is considered.
  The machine learning method proposed for this purpose is based on a modif
 ied zero-order optimization algorithm (golden-search algorithm) and bootst
 rapping technique. It has been validated in numerous simulations and real-
 world experiments in terms of its effectiveness in such aspects as: the im
 pact of environmental disturbances (wind gusts)\; flight with change in ma
 ss\; and change of sensory information sources in the auto-tuning procedur
 e.\n\n[]\n\nSpeaker(s): Dr. Wojciech Giernacki\n\nRoom: 3930\, Bldg: EC\, 
 10555 W Flagler St\, Miami\, Florida\, United States\, 33174\, Virtual: ht
 tps://events.vtools.ieee.org/m/418518
LOCATION:Room: 3930\, Bldg: EC\, 10555 W Flagler St\, Miami\, Florida\, Uni
 ted States\, 33174\, Virtual: https://events.vtools.ieee.org/m/418518
ORGANIZER:mesoo002@fiu.edu
SEQUENCE:22
SUMMARY:2024 IEEE Miami Section Invited Seminar Announcement: In-flight tun
 ing of UAVs controllers - from idea to patent protection by Dr. Wojciech G
 iernacki
URL;VALUE=URI:https://events.vtools.ieee.org/m/418518
X-ALT-DESC:Description: <br /><p><span class="OYPEnA text-strikethrough-non
 e text-decoration-none">With an increasing number of multirotor unmanned a
 erial vehicles (UAVs)\, solutions supporting the improvement in their prec
 ision of operation and safety of autonomous flights are gaining importance
 . They are particularly crucial in transportation tasks\, where control sy
 stems are required to provide a stable and controllable flight in various 
 environmental conditions\, especially after changing the total mass of the
  UAV (by adding extra load). In this presentation\, the problem of using o
 nly available basic sensory information for fast\, locally best\, iterativ
 e real-time auto-tuning of parameters of fixed-gain altitude controllers i
 s considered. The machine learning method proposed for this purpose is bas
 ed on a modified zero-order optimization algorithm (golden-search algorith
 m) and bootstrapping technique. It has been validated in numerous simulati
 ons and real-world experiments in terms of its effectiveness in such aspec
 ts as: the impact of environmental disturbances (wind gusts)\; flight with
  change in mass\; and change of sensory information sources in the auto-tu
 ning procedure.</span>&nbsp\;</p>\n<p><img src="https://events.vtools.ieee
 .org/vtools_ui/media/display/618bad93-b3a4-4d31-80be-95ce8512ca2b" alt="" 
 width="1037" height="1342"></p>
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