The IMOCO4.E reference framework for intelligent motion control systems

Sajid Mohamed, Gijs Van Der Veen, Hans Kuppens, Matias Vierimaa, Tassos Kanellos, Henry Stoutjesdijk, Riccardo Masiero, Kalle Määttä, Jan Wytze Van Der Weit, Gabriel Ribeiro, Ansgar Bergmann, Davide Colombo, Javier Arenas, Alfie Keary, Martin Goubej, Benjamin Rouxel, Pekka Kilpeläinen, Roberts Kadikis, Mikel Armendia, Petr BlahaJoep Stokkermans, Martin Čech, Arend Jan Beltman

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)

Abstract

Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial 'know-how' of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale 'Intelligent Motion Control under Industry4.E' (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework's applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.

Original languageEnglish
Title of host publicationIEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages8
ISBN (Electronic)9798350339918
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Article in a conference publication
Event28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 - Sinaia, Romania
Duration: 12 Sept 202315 Sept 2023

Publication series

SeriesIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2023-September
ISSN1946-0740

Conference

Conference28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023
Country/TerritoryRomania
CitySinaia
Period12/09/2315/09/23

Keywords

  • AI
  • cyber-physical systems
  • data management
  • digital twin
  • edge computing
  • mechatronics
  • motion control
  • reference framework
  • smart system integration

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