The IMOCO4.E reference framework for intelligent motion control systems
| dc.contributor.author | Čech, Martin | |
| dc.contributor.author | Goubej, Martin | |
| dc.date.accessioned | 2025-06-20T08:55:41Z | |
| dc.date.available | 2025-06-20T08:55:41Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2025-06-20T08:55:41Z | |
| dc.description.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. | en |
| dc.format | 8 | |
| dc.identifier.doi | 10.1109/ETFA54631.2023.10275410 | |
| dc.identifier.isbn | 979-8-3503-3991-8 | |
| dc.identifier.issn | 1946-0740 | |
| dc.identifier.obd | 43940656 | |
| dc.identifier.orcid | Čech, Martin 0000-0002-7673-4639 | |
| dc.identifier.orcid | Goubej, Martin 0000-0003-4073-0705 | |
| dc.identifier.uri | http://hdl.handle.net/11025/61597 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.relation.ispartofseries | 28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023 | |
| dc.subject | Motion control | en |
| dc.subject | robotics | en |
| dc.subject | Edge AI | en |
| dc.subject | Digital Twins | en |
| dc.subject | Automation | en |
| dc.title | The IMOCO4.E reference framework for intelligent motion control systems | en |
| dc.type | Stať ve sborníku (D) | |
| dc.type | STAŤ VE SBORNÍKU | |
| dc.type.status | Published Version | |
| local.files.count | 1 | * |
| local.files.size | 4601515 | * |
| local.has.files | yes | * |
| local.identifier.eid | 2-s2.0-85175441515 |
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