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 Blaha; Joep Stokkermans; Martin Čech; Arend Jan Beltman

doi:10.1109/ETFA54631.2023.10275410

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 Blaha

Joep Stokkermans, Martin Čech^*, Arend Jan Beltman

^*Corresponding author for this work

Itec BV
Sioux Technologies B.V.
G.N.T. Information Systems S.A.
Philips Medical Systems Nederland B.V.
CRIT s.r.l.
Normet Group Oy
Philips Consumer Lifestyle B.V.
Edilásio Carreira Da Silva Lda
Kion Group AG
WEG Automation Europe s.r.l.
Fagor AOTEK
University College Cork
University of West Bohemia
University of Modena and Reggio Emilia
Institute of Electronics and Computer Science (EDI)
Fundación Tekniker
Brno University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

13 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 language	English
Title of host publication	IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023
Publisher	IEEE Institute of Electrical and Electronic Engineers
Number of pages	8
ISBN (Electronic)	9798350339918
DOIs	https://doi.org/10.1109/ETFA54631.2023.10275410
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 - Sinaia, Romania Duration: 12 Sept 2023 → 15 Sept 2023

Publication series

Series	IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume	2023-September
ISSN	1946-0740

Conference

Conference	28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023
Country/Territory	Romania
City	Sinaia
Period	12/09/23 → 15/09/23

Funding

ACKNOWLEDGMENT This work was supported by ECSEL JU in the H2020 project IMOCO4.E, grant agreement No.101007311.

Keywords

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

Access to Document

10.1109/ETFA54631.2023.10275410

Cite this

Mohamed, S., Van Der Veen, G., Kuppens, H., Vierimaa, M., Kanellos, T., Stoutjesdijk, H., Masiero, R., Määttä, K., Van Der Weit, J. W., Ribeiro, G., Bergmann, A., Colombo, D., Arenas, J., Keary, A., Goubej, M., Rouxel, B., Kilpeläinen, P., Kadikis, R., Armendia, M., ... Beltman, A. J. (2023). The IMOCO4.E reference framework for intelligent motion control systems. In IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ETFA54631.2023.10275410

Mohamed, Sajid ; Van Der Veen, Gijs ; Kuppens, Hans et al. / The IMOCO4.E reference framework for intelligent motion control systems. IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023. IEEE Institute of Electrical and Electronic Engineers, 2023. (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Vol. 2023-September).

@inproceedings{d5a1dd8d71ae4fbb9174405776daac50,

title = "The IMOCO4.E reference framework for intelligent motion control systems",

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.",

keywords = "AI, cyber-physical systems, data management, digital twin, edge computing, mechatronics, motion control, reference framework, smart system integration",

author = "Sajid Mohamed and \{Van Der Veen\}, Gijs and Hans Kuppens and Matias Vierimaa and Tassos Kanellos and Henry Stoutjesdijk and Riccardo Masiero and Kalle M{\"a}{\"a}tt{\"a} and \{Van Der Weit\}, \{Jan Wytze\} and Gabriel Ribeiro and Ansgar Bergmann and Davide Colombo and Javier Arenas and Alfie Keary and Martin Goubej and Benjamin Rouxel and Pekka Kilpel{\"a}inen and Roberts Kadikis and Mikel Armendia and Petr Blaha and Joep Stokkermans and Martin {\v C}ech and Beltman, \{Arend Jan\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 ; Conference date: 12-09-2023 Through 15-09-2023",

year = "2023",

doi = "10.1109/ETFA54631.2023.10275410",

language = "English",

series = "IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

booktitle = "IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023",

address = "United States",

}

Mohamed, S, Van Der Veen, G, Kuppens, H, Vierimaa, M, Kanellos, T, Stoutjesdijk, H, Masiero, R, Määttä, K, Van Der Weit, JW, Ribeiro, G, Bergmann, A, Colombo, D, Arenas, J, Keary, A, Goubej, M, Rouxel, B, Kilpeläinen, P, Kadikis, R, Armendia, M, Blaha, P, Stokkermans, J, Čech, M & Beltman, AJ 2023, The IMOCO4.E reference framework for intelligent motion control systems. in IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023. IEEE Institute of Electrical and Electronic Engineers, IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, vol. 2023-September, 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023, Sinaia, Romania, 12/09/23. https://doi.org/10.1109/ETFA54631.2023.10275410

The IMOCO4.E reference framework for intelligent motion control systems. / Mohamed, Sajid; Van Der Veen, Gijs; Kuppens, Hans et al.
IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023. IEEE Institute of Electrical and Electronic Engineers, 2023. (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Vol. 2023-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

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AU - Mohamed, Sajid

AU - Van Der Veen, Gijs

AU - Kuppens, Hans

AU - Vierimaa, Matias

AU - Kanellos, Tassos

AU - Stoutjesdijk, Henry

AU - Masiero, Riccardo

AU - Määttä, Kalle

AU - Van Der Weit, Jan Wytze

AU - Ribeiro, Gabriel

AU - Bergmann, Ansgar

AU - Colombo, Davide

AU - Arenas, Javier

AU - Keary, Alfie

AU - Goubej, Martin

AU - Rouxel, Benjamin

AU - Kilpeläinen, Pekka

AU - Kadikis, Roberts

AU - Armendia, Mikel

AU - Blaha, Petr

AU - Stokkermans, Joep

AU - Čech, Martin

AU - Beltman, Arend Jan

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - AI

KW - cyber-physical systems

KW - data management

KW - digital twin

KW - edge computing

KW - mechatronics

KW - motion control

KW - reference framework

KW - smart system integration

UR - https://www.scopus.com/pages/publications/85175441515

U2 - 10.1109/ETFA54631.2023.10275410

DO - 10.1109/ETFA54631.2023.10275410

M3 - Conference article in proceedings

AN - SCOPUS:85175441515

T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

BT - IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

Y2 - 12 September 2023 through 15 September 2023

ER -

Mohamed S, Van Der Veen G, Kuppens H, Vierimaa M, Kanellos T, Stoutjesdijk H et al. The IMOCO4.E reference framework for intelligent motion control systems. In IEEE 28th International Conference on Emerging Technologies and Factory Automation, ETFA 2023. IEEE Institute of Electrical and Electronic Engineers. 2023. (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Vol. 2023-September). doi: 10.1109/ETFA54631.2023.10275410

The IMOCO4.E reference framework for intelligent motion control systems

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Keywords

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