{"oa":"1","abstract":[{"text":"Predictive control of power electronic systems always requires a suitable\r\nmodel of the plant. Using typical physics-based white box models, a trade-off\r\nbetween model complexity (i.e. accuracy) and computational burden has to be\r\nmade. This is a challenging task with a lot of constraints, since the model\r\norder is directly linked to the number of system states. Even though white-box\r\nmodels show suitable performance in most cases, parasitic real-world effects\r\noften cannot be modeled satisfactorily with an expedient computational load.\r\nHence, a Koopman operator-based model reduction technique is presented which\r\ndirectly links the control action to the system's outputs in a black-box\r\nfashion. The Koopman operator is a linear but infinite-dimensional operator\r\ndescribing the dynamics of observables of nonlinear autonomous dynamical\r\nsystems which can be nicely applied to the switching principle of power\r\nelectronic devices. Following this data-driven approach, the model order and\r\nthe number of system states are decoupled which allows us to consider more\r\ncomplex systems. Extensive experimental tests with an automotive-type permanent\r\nmagnet synchronous motor fed by an IGBT 2-level inverter prove the feasibility\r\nof the proposed modeling technique in a finite-set model predictive control\r\napplication.","lang":"eng"}],"publication":"arXiv:1804.00854","department":[{"_id":"101"}],"author":[{"first_name":"Sören","last_name":"Hanke","full_name":"Hanke, Sören"},{"first_name":"Sebastian","id":"47427","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz"},{"first_name":"Oliver","last_name":"Wallscheid","full_name":"Wallscheid, Oliver"},{"full_name":"Klus, Stefan","last_name":"Klus","first_name":"Stefan"},{"last_name":"Böcker","full_name":"Böcker, Joachim","first_name":"Joachim"},{"first_name":"Michael","full_name":"Dellnitz, Michael","last_name":"Dellnitz"}],"title":"Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives","citation":{"chicago":"Hanke, Sören, Sebastian Peitz, Oliver Wallscheid, Stefan Klus, Joachim Böcker, and Michael Dellnitz. “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives.” <i>ArXiv:1804.00854</i>, 2018.","ieee":"S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, and M. Dellnitz, “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives,” <i>arXiv:1804.00854</i>. 2018.","ama":"Hanke S, Peitz S, Wallscheid O, Klus S, Böcker J, Dellnitz M. Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. <i>arXiv:180400854</i>. 2018.","bibtex":"@article{Hanke_Peitz_Wallscheid_Klus_Böcker_Dellnitz_2018, title={Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives}, journal={arXiv:1804.00854}, author={Hanke, Sören and Peitz, Sebastian and Wallscheid, Oliver and Klus, Stefan and Böcker, Joachim and Dellnitz, Michael}, year={2018} }","apa":"Hanke, S., Peitz, S., Wallscheid, O., Klus, S., Böcker, J., &#38; Dellnitz, M. (2018). Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives. <i>ArXiv:1804.00854</i>.","short":"S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, M. Dellnitz, ArXiv:1804.00854 (2018).","mla":"Hanke, Sören, et al. “Koopman Operator-Based Finite-Control-Set Model Predictive Control for  Electrical Drives.” <i>ArXiv:1804.00854</i>, 2018."},"status":"public","main_file_link":[{"url":"https://arxiv.org/pdf/1804.00854.pdf","open_access":"1"}],"user_id":"47427","_id":"21634","date_created":"2021-04-19T16:17:30Z","language":[{"iso":"eng"}],"year":"2018","type":"preprint","date_updated":"2022-01-06T06:55:08Z"}