<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2085</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Reinforcement Learning Control of Six-Phase Permanent Magnet Synchronous Machines</title>
    <abstract language="eng">Control of multi-phase machines is a challenging topic due to the high number of controlled variables. Conventional control methods, such as field-oriented control (FOC), address this issue by introducing more control loops. This, however, increases the controller design complexity, while the tuning process can become cumbersome. To tackle the above, this paper proposes a deep deterministic policy gradient algorithm based controller that fulfills all the control objectives in one computational stage. More specifically, the proposed approach aims to learn a suitable current control policy for six-phase permanent magnet synchronous machines to simplify the commissioning of the drive system. In doing so, physical limitations of the drive system can be accounted for, while the compensation of imbalances between the two three-phase subsystems is rendered possible. After validating the training results in a controller-in-the-loop environment, test bench measurements are provided to demonstrate the effectiveness of the proposed controller. As shown, favorable steady-state and dynamic performance is achieved that is comparable to that of FOC. Therefore, as indicated by the presented results, reinforcement learning-based control approaches for multi-phase machines is a promising research area.</abstract>
    <parentTitle language="eng">2023 13th International Electric Drives Production Conference (EDPC)</parentTitle>
    <identifier type="doi">10.1109/edpc60603.2023.10372153</identifier>
    <identifier type="isbn">979-8-3503-7049-2</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T05:14:55Z","timestamp":1720415695191},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher"},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,29]]},"DOI":"10.1109\/edpc60603.2023.10372153","type":"proceedings-article","created":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T19:30:28Z","timestamp":1704137428000},"source":"Crossref","is-referenced-by-count":1,"title":["Reinforcement Learning Control of Six-Phase Permanent Magnet Synchronous Machines"],"prefix":"10.1109","author":[{"given":"Lara","family":"Broghammer","sequence":"first","affiliation":[{"name":"Institute ELSYS, Technische Hochschule,Nuremberg,Germany"}]},{"given":"Dennis","family":"Hufnagel","sequence":"additional","affiliation":[{"name":"Institute ELSYS, Technische Hochschule,Nuremberg,Germany"}]},{"given":"Tobias","family":"Schindler","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Chair of Electrical Drive Systems and Power Electronics,Germany"}]},{"given":"Michael","family":"Hoerner","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Chair of Electrical Drive Systems and Power Electronics,Germany"}]},{"given":"Petros","family":"Karamanakos","sequence":"additional","affiliation":[{"name":"Tampere University,Faculty of Information Technology and Communication Sciences,Finland"}]},{"given":"Armin","family":"Dietz","sequence":"additional","affiliation":[{"name":"Institute ELSYS, Technische Hochschule,Nuremberg,Germany"}]},{"given":"Ralph","family":"Kennel","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Chair of Electrical Drive Systems and Power Electronics,Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/28.464525"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2017.2722426"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2018.2823684"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/PRECEDE51386.2021.9680920"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SEST.2019.8849124"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/OJIA.2023.3284717"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3297274"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/OJIES.2021.3075521"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/OJPEL.2021.3065877"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IEMDC55163.2023.10238988"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.32657\/10356\/90191"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IEMDC.2015.7409032"},{"key":"ref13","volume-title":"Vector control of three-phase AC Machines","author":"Nguyen","year":"2015"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2019.2891533"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.2008.921557"},{"key":"ref16","article-title":"Matlab documentation: deep deterministic policy gradient (DDPG) agents"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/PRECEDE.2019.8753306"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/OPTIM-ACEMP50812.2021.9590016"}],"event":{"name":"2023 13th International Electric Drives Production Conference (EDPC)","location":"Regensburg, Germany","start":{"date-parts":[[2023,11,29]]},"end":{"date-parts":[[2023,11,30]]}},"container-title":["2023 13th International Electric Drives Production Conference (EDPC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10372133\/10372123\/10372153.pdf?arnumber=10372153","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T22:28:34Z","timestamp":1705098514000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10372153\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,29]]},"references-count":18,"URL":"http:\/\/dx.doi.org\/10.1109\/edpc60603.2023.10372153","relation":{},"subject":[],"published":{"date-parts":[[2023,11,29]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2023 13th International Electric Drives Production Conference (EDPC)</enrichment>
    <enrichment key="conference_place">Regensburg, Germany</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="ConferenceStatement">2023 13th International Electric Drives Production Conference (EDPC), 29-30 November 2023, Regensburg, Germany</enrichment>
    <author>Lara Broghammer</author>
    <author>Dennis Hufnagel</author>
    <author>Tobias Schindler</author>
    <author>Michael Hoerner</author>
    <author>Petros Karamanakos</author>
    <author>Armin Dietz</author>
    <author>Ralph Kennel</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multi-phase machines</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>current control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>permanent magnet synchronous machine (PMSM)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>power electronics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep reinforcement learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep deterministic policy gradient (DDPG)</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="institutes" number="">Institut für leistungselektronische Systeme ELSYS</collection>
    <collection role="Forschungsschwerpunkt" number="1">Energie &amp; Ressourcen</collection>
  </doc>
  <doc>
    <id>2089</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Reinforcement Learning Current Control of Permanent Magnet Synchronous Machines</title>
    <abstract language="eng">This paper presents a current control approach for permanent magnet synchronous machines (PMSMs) using the deep reinforcement learning algorithm deep deterministic policy gradient (DDPG). The proposed method is designed by examining different training setups regarding the reward function, the observation vector, and the actor neural network. In doing so, the impact of the different design factors on the steady-state and dynamic behavior of the system is assessed, thus facilitating the selection of the setup that results in the most favorable performance. Moreover, to provide the necessary insight into the controller design, the entire path from training the agent in simulation, through testing the control in a controller-in-the-loop (CIL) environment, to deployment on the test bench is described. Subsequently, experimental results are provided, which show the efficacy of the presented algorithm over a wide range of operating points. Finally, in an attempt to promote open science and expedite the use of deep reinforcement learning in power electronic systems, the trained agents, including the CIL model, are rendered openly available and accessible such that reproducibility of the presented approach is possible.</abstract>
    <parentTitle language="eng">2023 IEEE International Electric Machines &amp; Drives Conference (IEMDC)</parentTitle>
    <identifier type="doi">10.1109/IEMDC55163.2023.10238988</identifier>
    <identifier type="isbn">979-8-3503-9899-1</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T07:57:14Z","timestamp":1721894234807},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher"},{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,15]]},"DOI":"10.1109\/iemdc55163.2023.10238988","type":"proceedings-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T17:23:50Z","timestamp":1694021030000},"source":"Crossref","is-referenced-by-count":4,"title":["Deep Reinforcement Learning Current Control of Permanent Magnet Synchronous Machines"],"prefix":"10.1109","author":[{"given":"Tobias","family":"Schindler","sequence":"first","affiliation":[{"name":"Technical University of Munich,Chair of Electrical Drive Systems and Power Electronics,Germany"}]},{"given":"Lara","family":"Broghammer","sequence":"additional","affiliation":[{"name":"Institute ELSYS,Technische Hochschule,Nuremberg,Germany"}]},{"given":"Petros","family":"Karamanakos","sequence":"additional","affiliation":[{"name":"Tampere University,Faculty of Information Technology and Communication Sciences,Finland"}]},{"given":"Armin","family":"Dietz","sequence":"additional","affiliation":[{"name":"Institute ELSYS,Technische Hochschule,Nuremberg,Germany"}]},{"given":"Ralph","family":"Kennel","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Chair of Electrical Drive Systems and Power Electronics,Germany"}]}],"member":"263","reference":[{"key":"ref13","author":"nguyen","year":"2015","journal-title":"Vector Control of Three-Phase AC Machines"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/PRECEDE.2019.8753306"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/s20020426"},{"key":"ref14","article-title":"Matlab documentation: deep deterministic policy gradient (DDPG) agents","year":"0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/OPTIM-ACEMP50812.2021.9590016"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2022.3153845"},{"key":"ref2","article-title":"Learning dexterous in-hand manipulation","author":"andrychowicz","year":"2018"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/OJIA.2020.3020184"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148759"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100030"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/EDPC51184.2020.9388185"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2948387"},{"key":"ref7","article-title":"Continuous control with deep reinforcement learning","author":"lillicrap","year":"0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/OJPEL.2021.3065877"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","author":"mnih","year":"2015","journal-title":"Nature"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04301-9"},{"key":"ref6","first-page":"1","article-title":"Robust active learning multiple fault diagnosis of PMSM drives with sensorless control under dynamic operations and imbalanced datasets","author":"attestog","year":"2022","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/OJIES.2021.3075521"}],"event":{"name":"2023 IEEE International Electric Machines &amp; Drives Conference (IEMDC)","location":"San Francisco, CA, USA","start":{"date-parts":[[2023,5,15]]},"end":{"date-parts":[[2023,5,18]]}},"container-title":["2023 IEEE International Electric Machines &amp;amp; Drives Conference (IEMDC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10238840\/10238781\/10238988.pdf?arnumber=10238988","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:02:15Z","timestamp":1695664935000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10238988\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":18,"URL":"http:\/\/dx.doi.org\/10.1109\/iemdc55163.2023.10238988","relation":{},"subject":[],"published":{"date-parts":[[2023,5,15]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2023 IEEE International Electric Machines &amp; Drives Conference (IEMDC)</enrichment>
    <enrichment key="conference_place">San Francisco, CA, USA</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="ConferenceStatement">2023 IEEE International Electric Machines &amp; Drives Conference (IEMDC), 15-18 May 2023, San Francisco, CA, USA</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Tobias Schindler</author>
    <author>Lara Broghammer</author>
    <author>Petros Karamanakos</author>
    <author>Armin Dietz</author>
    <author>Ralph Kennel</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Open science</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>current control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>permanent magnet synchronous machine (PMSM)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>power electronics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep reinforcement learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep deterministic policy gradient (DDPG)</value>
    </subject>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="institutes" number="">Institut für leistungselektronische Systeme ELSYS</collection>
    <collection role="Forschungsschwerpunkt" number="1">Energie &amp; Ressourcen</collection>
  </doc>
  <doc>
    <id>1664</id>
    <completedYear>2021</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">UltraZohm — An Open-Source Rapid Control Prototyping Platform for Power Electronic Systems</title>
    <abstract language="deu">This paper presents two rapid control prototyping (RCP) use cases facilitated by the open-source platform UltraZohm. The openly available UltraZohm development frame-&#13;
work eases the transition from simulation to the test bench. The framework offers the integration of automatic code generation for field-programmable gate arrays (FPGAs), either by using Simulink models based on the HDL Coder, or by synthesizing C++ code into VHDL via the Vivado high-level synthesis tool. The first use case focuses on the implementation details of an on-chip controller-in-the-loop setup, where a permanent magnetsynchronous machine is emulated in the FPGA with a sampling frequency of 2 MHz. The second use case presents an efficient real-time implemen-tation of the sphere decoding algorithm employed to solve the long-horizon finite control set model predictive control problem for a three-level neutral point clamped inverter driving an induction machine. Experimental results based on a small-scale prototype confirm that the algorithm can be executed in real time on the FPGA, with an execution time of a few tens of microseconds. Both use cases highlight the benefits of using a high-performance RCP platform for research in power electronics and their control.</abstract>
    <parentTitle language="eng">2021 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) &amp; 2021 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)</parentTitle>
    <identifier type="doi">10.1109/optim-acemp50812.2021.9590016</identifier>
    <identifier type="issn">1842-0133</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:33:01Z","timestamp":1709339581208},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"DOI":"10.1109\/optim-acemp50812.2021.9590016","type":"proceedings-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T19:23:05Z","timestamp":1636140185000},"source":"Crossref","is-referenced-by-count":8,"title":["UltraZohm\u2014An Open-Source Rapid Control Prototyping Platform for Power Electronic Systems"],"prefix":"10.1109","author":[{"given":"Eyke","family":"Liegmann","sequence":"first","affiliation":[]},{"given":"Tobias","family":"Schindler","sequence":"additional","affiliation":[]},{"given":"Petros","family":"Karamanakos","sequence":"additional","affiliation":[]},{"given":"Armin","family":"Dietz","sequence":"additional","affiliation":[]},{"given":"Ralph","family":"Kennel","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"1622","article-title":"A design method of an embedded real-time simulator for electric drives using low-cost system-on-chip platform","author":"perera","year":"2021","journal-title":"Proc Int Exhib and Conf for Power Electron Intell Motion Renew Energy and Energy Manag"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPEL.2014.2306939"},{"key":"ref12","first-page":"1544","article-title":"High-level synthesis of a long horizon model predictive control algorithm for an FPGA","author":"bin khalid","year":"2020","journal-title":"Proc Int Exhib and Conf for Power Electron Intell Motion Renew Energy and Energy Manag"},{"key":"ref13","first-page":"1823","article-title":"FPGA-implementation friendly long-horizon finite control set model predictive control for high-power electronic systems","author":"baltruweit","year":"2021","journal-title":"Proc IEEE Energy Convers Congr Expo Asia"},{"key":"ref14","first-page":"6245","article-title":"Indirect model predictive control for a grid-tied three-level neutral point clamped converter with an LCL filter","author":"rossi","year":"2020","journal-title":"Proc IEEE Energy Convers Congr Expo"},{"key":"ref15","first-page":"1","article-title":"Real-time inference of neural networks on FPGAs for motor control applications","author":"schindler","year":"2020","journal-title":"Int Elect Drives Prod Conf"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.23919\/EPE.2019.8914947"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/electronics7100219"},{"key":"ref4","first-page":"1","article-title":"UltraZohm&amp;#x2014;A powerful real-time computation platform for MPC and multilevel inverters","author":"wendel","year":"2019","journal-title":"Proc IEEE Int Symp Pred Control of Elect Drives and Power Electron"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/OJPEL.2021.3065877"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.23919\/IPEC.2018.8507630"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/WEMDCD.2017.7947749"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/en13051074"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ISIE.2019.8781320"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/OJIES.2021.3075521"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/OJIA.2020.3020184"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IECON.2018.8591256"}],"event":{"name":"2021 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) &amp; 2021 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)","location":"Brasov, Romania","start":{"date-parts":[[2021,9,2]]},"end":{"date-parts":[[2021,9,3]]}},"container-title":["2021 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) &amp;amp; 2021 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9588333\/9590007\/09590016.pdf?arnumber=9590016","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:50:01Z","timestamp":1652201401000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9590016\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,2]]},"references-count":17,"URL":"http:\/\/dx.doi.org\/10.1109\/optim-acemp50812.2021.9590016","relation":{},"subject":[],"published":{"date-parts":[[2021,9,2]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2021 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) &amp; 2021 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)</enrichment>
    <enrichment key="conference_place">Brasov, Romania</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <author>Eyke Liegmann</author>
    <author>Tobias Schindler</author>
    <author>Petros Karamanakos</author>
    <author>Armin Dietz</author>
    <author>Ralph Kennel</author>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="institutes" number="">Institut für leistungselektronische Systeme ELSYS</collection>
    <collection role="Forschungsschwerpunkt" number="1">Energie &amp; Ressourcen</collection>
  </doc>
  <doc>
    <id>1673</id>
    <completedYear>2020</completedYear>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Real-Time Inference of Neural Networks on FPGAs for Motor Control Applications</title>
    <abstract language="deu">Machine learning algorithms are increasingly used in industrial applications for a multitude of use-cases. However, using them in control tasks is a challenge due to real-time requirements and limited resources. In this paper, an implementation scheme for real-time inference of multilayer perceptron (MLP) neural networks on FPGAs is proposed. Design constraints for using MLPs in reinforcement learning agents for motor control applications are derived and accounted for in the implementation. Two MLP architectures are evaluated on an FPGA, and the timing and resource-usage data are reported. The real-time capability of the implementation for motor control applications is investigated for standard control frequencies. It is shown by experimental validation that real-time interference with an area-efficient implementation for motor control applications is achievable. Therefore, the proposed implementation scheme can be applied to deep reinforcement learning controllers with hard real-time requirements.</abstract>
    <parentTitle language="eng">2020 10th International Electric Drives Production Conference (EDPC)</parentTitle>
    <identifier type="doi">10.1109/edpc51184.2020.9388185</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T13:06:33Z","timestamp":1715173593711},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:00:00Z","timestamp":1607385600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,8]]},"DOI":"10.1109\/edpc51184.2020.9388185","type":"proceedings-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T20:25:16Z","timestamp":1617222316000},"source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Inference of Neural Networks on FPGAs for Motor Control Applications"],"prefix":"10.1109","author":[{"given":"Tobias","family":"Schindler","sequence":"first","affiliation":[]},{"given":"Armin","family":"Dietz","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3039902.3039915"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890150"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/PRECEDE.2019.8753306"},{"key":"ref13","article-title":"Zynq UltraScale+ MPSoC Data Sheet: Overview","year":"0"},{"key":"ref14","article-title":"Reinforcement Learning: An Introduction","author":"sutton","year":"2018"},{"key":"ref15","article-title":"A Deeper Look at Experience Replay","author":"zhang","year":"2017"},{"key":"ref16","article-title":"Hindsight Experience Replay","author":"andrychowicz","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-020-03342-7"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-11398-7"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/PRECEDE.2017.8071270"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2020.2969116"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref5","first-page":"1","article-title":"Comparison of Reinforcement Learning Algorithms for Speed Ripple Reduction of Permanent Magnet Synchronous Motor","author":"schindler","year":"2019","journal-title":"Innovative Small Drives and Micro-Motor Systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/IEMDC.2019.8785109"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMAG.2002.802410"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847265"}],"event":{"name":"2020 10th International Electric Drives Production Conference (EDPC)","location":"Ludwigsburg, Germany","start":{"date-parts":[[2020,12,8]]},"end":{"date-parts":[[2020,12,9]]}},"container-title":["2020 10th International Electric Drives Production Conference (EDPC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9388108\/9388173\/09388185.pdf?arnumber=9388185","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:57:23Z","timestamp":1656345443000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9388185\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,8]]},"references-count":19,"URL":"http:\/\/dx.doi.org\/10.1109\/edpc51184.2020.9388185","relation":{},"subject":[],"published":{"date-parts":[[2020,12,8]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2020 10th International Electric Drives Production Conference (EDPC)</enrichment>
    <enrichment key="conference_place">Ludwigsburg, Germany</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <author>Tobias Schindler</author>
    <author>Armin Dietz</author>
    <collection role="institutes" number="">Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik</collection>
    <collection role="institutes" number="">Institut für leistungselektronische Systeme ELSYS</collection>
    <collection role="Forschungsschwerpunkt" number="1">Energie &amp; Ressourcen</collection>
  </doc>
</export-example>
