Intelligent Motion Control under Industry 4.E

Risultati finali

Requirements for advanced motion control (final iteration)

The deliverable is dedicated to description of the final requirements for the control layer based on inputs from D24 D41 D61 and D71 Based on the initial test results the final report will present only the updates if any of the initial requirements defined in D41

Perception and instrumentation Layer requirements and specifications (first iteration)

The deliverable is dedicated to description of the initial requirements for the perception andinstrumentation layer which are partially also based on inputs from D21 and D22 while working inparallel and considering D71 and D72 This report will summarize the perception and instrumentationlayer requirements specific for the relevant BBs pilots demonstrators and usecases

Integral (system level) requirements for valuable twinning methods (first iteration)

The deliverable D51 will provide requirements and specifications on digital twins and their supporting technologies It will rely on inputs from D21 and D22 The requirements from pilots demonstrators and use cases will be gathered and analysed there from the perspective of BB6 BB8 and BB9 which will be prepared in WP5

Perception and instrumentation Layer requirements and specifications (final iteration)

The deliverable is dedicated to description of the final requirements for the perception and instrumentation layer Based on the initial test results the final report will present only the updates if any of the initial requirements defined in D31

General specification and design of IMOCO4.E reference framework

Based on D23 the final requirements deliverables D32 D42 D52 and the IMOCO4E methodology outlined in D61 this deliverable presents a reference architecture of the IMOCO4E framework at an abstract level This will be the basic but extensible open and modular framework to be realised demonstrated and validated in the IMOCO4E project

State-of-the-art methods in Digital Twinning for motion-driven high-tech applications

This deliverable presents a market scan also among consortium partners and literature survey on the state of the art in digital twinning solutions and the application of AI Digital twinning and AI will be considered at several levels ranging from modulelevel to productionlinelevel The report will also describe emerging technologies in this field and identify development directions for IMOCO4E

Requirements for advanced motion control (first iteration)

The deliverable is dedicated to description of the initial requirements for the control layer Layer 2 which are partially also based on inputs from D21 and D22 while working in parallel and considering D71 and D72 This report will summarize the control layer requirements specific for the relevant BBs pilots demonstrators and usecases

Integral (system level) requirements for valuable twinning methods (second iteration)

This deliverable will contain an extension of requirements and specifications that were prepared in D 51 Only needed revisions of the existing ones and potentially additional ones will be placed there as they can be revealed in the early stage of work in Tasks 52 to 57

Overall requirements on IMOCO4.E reference framework

This deliverable describes the requirements on the IMOCO4E reference framework based on inputs from D21 D22 D32 D42 and D51 The deliverable provides requirements on hardware and software building blocks and the instrumentation layer but also on digital twinning the application of AI and ultimately the interfacing between all the IMOCO4E framework components

Needs for future smart production in Europe from the mechatronics and robotic point of view

Based on inputs from D21 knowledge among consortium partners and market knowledge this deliverable identifies the solutions required to expedite the introduction of smart production in Europe eg by addressing existing bottlenecks The report will provide the key technologies that IMOCO4E should focus on to benefit a competitive European manufacturing ecosystem

Project Website Functional

A website of IMOCO4E project will be established to inform about IMOCO4E interests progress technicalresults It will include all public reports information about IMOCO4E consortium It will also serve as anexternal communication platform for the consortium partners

Pubblicazioni

Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors

Autori: van Meer, Max, González, Rodrigo A., Witvoet, Gert, Oomen, Tom
Pubblicato in: 2023
Editore: IEEE

Frequency Domain Identification of Multirate Systems: A Lifted Local Polynomial Modeling Approach

Autori: van Haren, Max; Blanken, Lennart; Oomen, Tom
Pubblicato in: 2022
Editore: IEEE 61st Conference on Decision and Control 2022 (CDC)
DOI: 10.5281/zenodo.6982575

Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks: With Application to a Printer with Friction

Autori: Max van Meer; Maurice Poot; Jim Portegies; Tom Oomen
Pubblicato in: 2022
Editore: IFAC Modeling, Estimation, and Control Conference (MECC 2022)
DOI: 10.5281/zenodo.7291337

Brief Announcement: Optimized GPU-accelerated Feature Extraction for ORB-SLAM Systems

Autori: Filippo Muzzini, Nicola Capodieci , Roberto Cavicchioli, Benjamin Rouxel
Pubblicato in: 2023
Editore: Association for Computing Machinery
DOI: 10.1145/3558481.3591310

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Autori: Alessio Masola, Nicola Capodieci, Benjamin Rouxel, Giorgia Franchini, Roberto Cavicchioli
Pubblicato in: 2023
Editore: IEEE
DOI: 10.1109/rtcsa58653.2023.00026

Memory-Aware Latency Prediction Model for Concurrent Kernels in Partitionable GPUs: Simulations and Experiments

Autori: Alessio Masola, Nicola Capodieci, Roberto Cavicchioli, Ignacio Sanudo Olmedo, Benjamin Rouxel
Pubblicato in: 2023
Editore: Springer Cham
DOI: 10.1007/978-3-031-43943-8

Ablation Study of a Person Re-Identification on a Mobile Robot Using a Depth Camera

Autori: Sebastian Flores; Jana Jost
Pubblicato in: 2022
Editore: IEEE
DOI: 10.1109/isie51582.2022.9831503

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Autori: Max van Haren; Maurice Poot; Dragan Kostić; Robin van Es; Jim Portegies; Tom Oomen
Pubblicato in: 2022
Editore: IEEE
DOI: 10.1109/amc51637.2022.9729327

Cascaded Calibration of Mechatronic Systems via Bayesian Inference

Autori: Van Meer, Max; Deniz, Emre; Witvoet, Gert; Oomen, Tom
Pubblicato in: Numero 1, 2023
Editore: IFAC
DOI: 10.48550/arxiv.2304.03136

NC controlled robot for adaptive and constant force 3D polishing

Autori: Diego Gonzalez; Mikel Armendia
Pubblicato in: 2022
Editore: IEEE
DOI: 10.1109/ETFA52439.2022.9921666

Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

Autori: Max van Meer; Gert Witvoet; Tom Oomen
Pubblicato in: 2022
Editore: IFAC Modeling, Estimation and Control Conference
DOI: 10.5281/zenodo.7291453

A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems

Autori: Max van Haren, Lennart Blanken and Tom Oomen
Pubblicato in: Max van Haren, Lennart Blanken and Tom Oomen, 2023
Editore: IFAC

Person Re-Identification on a Mobile Robot Using a Depth Camera

Autori: Sebastian Flores; Jana Jost
Pubblicato in: 2022
Editore: IEEE
DOI: 10.1109/isie51582.2022.9831515

Position-Dependent Snap Feedforward: A Gaussian Process Framework

Autori: van Haren, Max; Poot, Maurice; Portegies, Jim; Oomen, Tom
Pubblicato in: 2022
Editore: 2022 IEEE American Control Conference (ACC)
DOI: 10.5281/zenodo.6351295

Time-sensitive autonomous architectures

Autori: Donato Ferraro, Luca Palazzi, Federico Gavioli, Michele Guzzinati, Andrea Bernardi, Benjamin Rouxel, Paolo Burgio, Marco Solieri
Pubblicato in: Real-Time Systems, 2023, ISSN 0922-6443
Editore: Kluwer Academic Publishers
DOI: 10.1007/s11241-023-09404-2

Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems

Autori: Max van Haren, Leonid Mirkin, Lennart Blanken and Tom Oomen
Pubblicato in: IEEE Control Systems Letters, 2023, ISSN 2475-1456
Editore: IEEE

Leveraging Satisfiability Modulo Theory Solvers for Verification of Neural Networks in Predictive Maintenance Applications

Autori: Dario Guidotti, Laura Pandolfo and Luca Pulina
Pubblicato in: Information, 2023, 2023, ISSN 2078-2489
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/info14070397

Risultati finali

Pubblicazioni

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