{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:57:42Z","timestamp":1760151462361,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T00:00:00Z","timestamp":1647820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.<\/jats:p>","DOI":"10.3390\/sym14030626","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T21:48:42Z","timestamp":1647899322000},"page":"626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multistep Model Predictive Control for Electrical Drives\u2014A Fast Quadratic Programming Solution"],"prefix":"10.3390","volume":"14","author":[{"given":"Haotian","family":"Xie","sequence":"first","affiliation":[{"name":"Electrical Drive Systems and Power Electronics, Technical University of Munich, 80333 M\u00fcnchen, Germany"}]},{"given":"Jianming","family":"Du","sequence":"additional","affiliation":[{"name":"Laboratory of Renewable Energy Systems, University of Applied Sciences Munich, 80335 M\u00fcnchen, Germany"}]},{"given":"Dongliang","family":"Ke","sequence":"additional","affiliation":[{"name":"National Local Joint Engineering Research Center for Electrical Drives and Power Electronics, Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Jinjiang 362200, China"}]},{"given":"Yingjie","family":"He","sequence":"additional","affiliation":[{"name":"Electrical Drive Systems and Power Electronics, Technical University of Munich, 80333 M\u00fcnchen, Germany"}]},{"given":"Fengxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"National Local Joint Engineering Research Center for Electrical Drives and Power Electronics, Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Jinjiang 362200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-6818","authenticated-orcid":false,"given":"Christoph","family":"Hackl","sequence":"additional","affiliation":[{"name":"Laboratory of Renewable Energy Systems, University of Applied Sciences Munich, 80335 M\u00fcnchen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1410-4121","authenticated-orcid":false,"given":"Jos\u00e9","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Engineering Sciences, Universidad Andres Bello, Santiago 7500971, Chile"}]},{"given":"Ralph","family":"Kennel","sequence":"additional","affiliation":[{"name":"Electrical Drive Systems and Power Electronics, Technical University of Munich, 80333 M\u00fcnchen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/j.automatica.2014.10.128","article-title":"Model Predictive Control: Recent Developments and Future Promise","volume":"50","author":"Mayne","year":"2014","journal-title":"Automatica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TAC.2002.808470","article-title":"Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations","volume":"48","author":"Rao","year":"2003","journal-title":"IEEE Trans. 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