@article{PeitzGraelerHenkeetal., author = {Peitz, Sebastian and Gr{\"a}ler, Manuel and Henke, Christian and Hessel-von Molo, Mirko and Dellnitz, Michael and Tr{\"a}chtler, Ansgar}, title = {Multiobjective Model Predictive Control of an Industrial Laundry}, series = {Procedia Technology}, journal = {Procedia Technology}, number = {26}, pages = {483 -- 490}, abstract = {In a wide range of applications, it is desirable to optimally control a system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective optimal control problem where, instead of computing a single optimal solution, the set of optimal compromises, the so-called Pareto set, has to be approximated. When it is not possible to compute the entire control trajectory in advance, for instance due to uncertainties or unforeseeable events, model predictive control methods can be applied to control the system during operation in real time. In this article, we present an algorithm for the solution of multiobjective model predictive control problems. In an offline scenario, it can be used to compute the entire set of optimal compromises whereas in a real time scenario, one optimal compromise is computed according to an operator's preference. The results are illustrated using the example of an industrial laundry. A logistics model of the laundry is developed and then utilized in the optimization routine. Results are presented for an offline as well as an online scenario}, language = {en} } @article{KlusGelssPeitzetal., author = {Klus, Stefan and Gelß, Patrick and Peitz, Sebastian and Sch{\"u}tte, Christof}, title = {Tensor-based dynamic mode decomposition}, series = {Nonlinearity}, volume = {31}, journal = {Nonlinearity}, number = {7}, publisher = {IOP Publishing Ltd \& London Mathematical Society}, doi = {10.1088/1361-6544/aabc8f}, language = {en} } @article{KlusNueskePeitzetal., author = {Klus, Stefan and N{\"u}ske, Feliks and Peitz, Sebastian and Niemann, Jan-Hendrik and Clementi, Cecilia and Sch{\"u}tte, Christof}, title = {Data-driven approximation of the Koopman generator: Model reduction, system identification, and control}, series = {Physica D: Nonlinear Phenomena}, volume = {406}, journal = {Physica D: Nonlinear Phenomena}, doi = {10.1016/j.physd.2020.132416}, language = {en} } @article{DellnitzHuellermeierLueckeetal., author = {Dellnitz, Michael and H{\"u}llermeier, Eyke and L{\"u}cke, Marvin and Ober-Bl{\"o}baum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, title = {Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning}, series = {SIAM Journal on Scientific Computing}, volume = {45}, journal = {SIAM Journal on Scientific Computing}, number = {2}, doi = {10.1137/21M1412682}, pages = {A579 -- A595}, language = {en} }