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PRODID:IEEE vTools.Events//EN
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DTSTART:20231105T010000
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DTSTAMP:20240115T175658Z
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DTSTART;TZID=America/St_Johns:20230920T120000
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DESCRIPTION:Nonlinear Model Predictive Control (NMPC) is an MPC variant tha
 t uses the nonlinear system model in its prediction. NMPC scheme has been 
 recently used to control different autonomous mobile robots due to its cap
 ability to handle complex multi-input multi-output control problems\nwhile
  addressing input and state constraints. NMPC showed promising stabilizati
 on and tracking performance\, in addition to\, impressive collision and ob
 stacle avoidance performance. The control problem is formulated as an opti
 mization problem with a predefined objective function\, where the minimiza
 tion of this function over a finite time horizon leads to the optimal cont
 rol input values. This talk will (1) demonstrate the design of the NMPC sc
 heme for an autonomous micro aerial vehicle (MAV)\, (2) provide the necess
 ary conditions for stability guarantee of the closed-loop system\, and (3)
  present the implementation of the control scheme on MATLAB using CasADi t
 oolbox and present the real-time experimental implementation using Robot O
 perating System (ROS). Eventually\, the presentation will highlight the po
 ssible directions to extend the given NMPC schemes for learning-based mode
 ls.\n\nSpeaker(s): Hakim\n\nVirtual: https://events.vtools.ieee.org/m/3731
 00
LOCATION:Virtual: https://events.vtools.ieee.org/m/373100
ORGANIZER:oscar.desilva@mun.ca
SEQUENCE:24
SUMMARY:IEEE NL Technical Talk: Nonlinear Model Predictive Control for Auto
 nomous Aerial Vehicles: Design\, Analysis\, and Real-time Validation
URL;VALUE=URI:https://events.vtools.ieee.org/m/373100
X-ALT-DESC:Description: <br /><p>Nonlinear Model Predictive Control (NMPC) 
 is an MPC variant that uses the nonlinear system model in its prediction. 
 NMPC scheme has been recently used to control different autonomous mobile 
 robots due to its capability to handle complex multi-input multi-output co
 ntrol problems<br />while addressing input and state constraints. NMPC sho
 wed promising stabilization and tracking performance\, in addition to\, im
 pressive collision and obstacle avoidance performance. The control problem
  is formulated as an optimization problem with a predefined objective func
 tion\, where the minimization of this function over a finite time horizon 
 leads to the optimal control input values. This talk will (1) demonstrate 
 the design of the NMPC scheme for an autonomous micro aerial vehicle (MAV)
 \, (2) provide the necessary conditions for stability guarantee of the clo
 sed-loop system\, and (3) present the implementation of the control scheme
  on MATLAB using CasADi toolbox and present the real-time experimental imp
 lementation using Robot Operating System (ROS). Eventually\, the presentat
 ion will highlight the possible directions to extend the given NMPC scheme
 s for learning-based models.</p>
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