Real-Time Virtualization For Industrial Automation

Scordino, Claudio; Savino, Ida Maria; Cuomo, Luca; Miccio, Luca; Tagliavini, Andrea; Bertogna, Marko; Solieri, Marco

doi:10.1109/etfa46521.2020.9211890

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…Depending on the requirements of the respective application, it is to be decided where to execute it the best. The approach of edge and cloud can be seen, for example, in the current development of the virtualization of industrial devices such as PLCs [ 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Rosenberger

Selig

Ristić

et al. 2023

Sensors

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With the convergence of information technology (IT) and operational technology (OT) in Industry 4.0, edge computing is increasingly relevant in the context of the Industrial Internet of Things (IIoT). While the use of simulation is already the state of the art in almost every engineering discipline, e.g., dynamic systems, plant engineering, and logistics, it is less common for edge computing. This work discusses different use cases concerning edge computing in IIoT that can profit from the use of OT simulation methods. In addition to enabling machine learning, the focus of this work is on the virtual commissioning of data stream processing systems. To evaluate the proposed approach, an exemplary application of the middleware layer, i.e., a multi-agent reinforcement learning system for intelligent edge resource allocation, is combined with a physical simulation model of an industrial plant. It confirms the feasibility of the proposed use of simulation for virtual commissioning of an industrial edge computing system using Hardware-in-the-Loop. In summary, edge computing in IIoT is highlighted as a new application area for existing simulation methods from the OT perspective. The benefits in IIoT are exemplified by various use cases for the logic or middleware layer using physical simulation of the target environment. The relevance for real-life IIoT systems is confirmed by an experimental evaluation, and limitations are pointed out.

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Section: Introductionmentioning

confidence: 99%

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Rosenberger

Selig

Ristić

et al. 2023

Sensors

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“…• RT simulation of mechatronic systems, which requires seamless model reduction techniques and complex model verification [41].…”

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confidence: 99%

Pushing Mechatronic Applications to the Limits via Smart Motion Control

2021

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Modern machines strive to run at limit performance and dependability while their operational area and size are getting restricted. To achieve those objectives, often swift integration of custom-made subsystems is required, either actuators, sensors, electronic, or SW modules. Such a diverse suite of elements needs specific approaches and tools for fast optimization and adjustment following model-based system engineering (MBSE) and digital twinning principles. The large-scale I-MECH project was an industry-driven initiative striving to give a scientific response to those demands. The intermediate results were summarized in the authors’ previous work. The purpose of this paper is to report on final project results, namely specific performance achievements and figures based on measurable KPIs. After a brief description of key technologies, special focus is given to industrial printing technology based on a generic substrate carrier. However, it is shown that similar and consistent methodology can be applicable in many other industrial domains, such as semiconductors, healthcare robotics, machining, packaging, etc. Thus, the main merit of this survey is a holistic approach to motion control design.

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Supporting AI-powered real-time cyber-physical systems on heterogeneous platforms via hypervisor technology

et al. 2023

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The heavy use of machine learning algorithms in safety-critical systems poses serious questions related to safety, security, and predictability issues, requiring novel architectural approaches to guarantee such properties. This paper presents an architecture solution that leverages heterogeneous platforms and virtualization technologies to support AI-powered applications consisting of modules with mixed criticalities and safety requirements. The hypervisor exploits the security features of the Xilinx ZCU104 MPSoCs to create two isolated execution environments: a high performance domain running deep learning algorithms under the Linux operating system and a safety-critical domain running control and monitoring functions under the freeRTOS real-time operating system. The proposed approach is validated by a use case consisting of an unmanned aerial vehicle capable of tracking moving targets using a deep neural network accelerated on the FGPA available on the platform.

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Real-Time Virtualization For Industrial Automation

Cited by 10 publications

References 14 publications

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Virtual Commissioning of Distributed Systems in the Industrial Internet of Things

Pushing Mechatronic Applications to the Limits via Smart Motion Control

Supporting AI-powered real-time cyber-physical systems on heterogeneous platforms via hypervisor technology

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