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SUMMARY:Embedded optimization and nonlinear model predictive control (
 K. Graichen\, FAU\, Germany)
UID:cfd3ecf9-ce4e-4641-84ee-e1a01f6c9df2
DESCRIPTION:Embedded optimization and nonlinear model predictive contr
 ol Speaker: Prof. Dr.-Ing. Knut Graichen Affiliation: FAU\, Germany Zo
 om: Meeting ID: 653 4383 6219 \, Passcode: 186010 Abstract: Optimizati
 on algorithms have become powerful and established tools for the contr
 ol and optimization of dynamical systems\, e.g. in the context of stat
 e estimation\, parameter identification and in particular model predic
 tive control (MPC). MPC is an advanced control scheme for linear and n
 onlinear multiple-input systems that allows to consider system constra
 ints as well as additional control objectives such as energy efficienc
 y. However\, a well-known drawback of MPC especially for nonlinear sys
 tems is its high computational effort that is related to the online so
 lution of the underlying optimization problem. This challenge is the m
 ore severe if mechatronic systems with sampling times in the range of 
 (sub-)milliseconds and weak computational hardware are considered. In 
 order to implement optimization methods and MPC for real-time control 
 purposes therefore requires the design of embedded optimization algori
 thms with limited complexity while ensuring real-time feasibility and 
 computational efficiency. The talk addresses the field of embedded rea
 l-time optimization and its application to MPC for the control of dyna
 mical systems. It also gives an introduction into the open-source tool
 box GRAMPC (gradient-based augmented Lagrangian framework for embedded
  NMPC) developed at the Chair of Automatic Control and demonstrates it
 s performance in comparison with state-of-the-art MPC solvers. Selecte
 d mechatronic examples running in the millisecond range are presented 
 and some research extensions in connection with model predictive contr
 ol are highlighted. Applied Analysis
DTSTART:20210526T083000Z
DTEND:20210526T093000Z
LOCATION:Online
DTSTAMP:20260909T144901Z
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