This talk shows how embedded optimization - i.e. autonomous optimization algorithms receiving data, solving problems, and sending answers continuously - are able to address challenging control problems. When nonlinear differential equation models are used to predict and optimize future system behaviour, one speaks of Nonlinear Model Predictive Control (NMPC).The talk presents experimental applications of NMPC to time and energy optimal control of mechatronic systems and discusses some of the algorithmic tricks that make NMPC optimization rates up to 1 MHz possible. Finally, we present on particular challenging application, tethered flight for airborne wind energy systems.
Organizers: Sebastian Trimpe