Английская Википедия:Cyber–physical system

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Шаблон:Short description Шаблон:Anchor Cyber–Physical System (CPS) are integrations of computation with physical processes.[1] In cyber–physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context.[2][3] CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science.[4][5][6][7] The process control is often referred to as embedded systems. In embedded systems, the emphasis tends to be more on the computational elements, and less on an intense link between the computational and physical elements. CPS is also similar to the Internet of Things (IoT), sharing the same basic architecture; nevertheless, CPS presents a higher combination and coordination between physical and computational elements.[4][8]

Examples of CPS include smart grid, autonomous automobile systems, medical monitoring, industrial control systems, robotics systems, recycling[4] and automatic pilot avionics.[3][9] Precursors of cyber–physical systems can be found in areas as diverse as aerospace, automotive, chemical processes, civil infrastructure, energy, healthcare, manufacturing, transportation, entertainment, and consumer appliances.[4][9]

Overview

Unlike more traditional embedded systems, a full-fledged CPS is typically designed as a network of interacting elements with physical input and output instead of as standalone devices.[5] The notion is closely tied to concepts of robotics and sensor networks with intelligence mechanisms proper of computational intelligence leading the pathway. Ongoing advances in science and engineering improve the link between computational and physical elements by means of intelligent mechanisms, increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber–physical systems.[10] This will broaden the potential of cyber–physical systems in several directions, including: intervention (e.g., collision avoidance); precision (e.g., robotic surgery and nano-level manufacturing); operation in dangerous or inaccessible environments (e.g., search and rescue, firefighting, and deep-sea exploration); coordination (e.g., air traffic control, war fighting); efficiency (e.g., zero-net energy buildings); and augmentation of human capabilities (e.g. in healthcare monitoring and delivery).[11]

Mobile cyber–physical systems

Mobile cyber–physical systems, in which the physical system under study has inherent mobility, are a prominent subcategory of cyber–physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals. The rise in popularity of smartphones has increased interest in the area of mobile cyber–physical systems. Smartphone platforms make ideal mobile cyber–physical systems for a number of reasons, including:

For tasks that require more resources than are locally available, one common mechanism for rapid implementation of smartphone-based mobile cyber–physical system nodes utilizes the network connectivity to link the mobile system with either a server or a cloud environment, enabling complex processing tasks that are impossible under local resource constraints.[13] Examples of mobile cyber–physical systems include applications to track and analyze COШаблон:Sub emissions,[14] detect traffic accidents, insurance telematics[15] and provide situational awareness services to first responders,[16][17] measure traffic,[18] and monitor cardiac patients.[19]

Examples

Common applications of CPS typically fall under sensor-based communication-enabled autonomous systems. For example, many wireless sensor networks monitor some aspect of the environment and relay the processed information to a central node. Other types of CPS include smart grid,[20] autonomous automotive systems, medical monitoring, process control systems, distributed robotics, recycling[4] and automatic pilot avionics.

A real-world example of such a system is the Distributed Robot Garden at MIT in which a team of robots tend a garden of tomato plants. This system combines distributed sensing (each plant is equipped with a sensor node monitoring its status), navigation, manipulation and wireless networking.[21]

A focus on the control system aspects of CPS that pervade critical infrastructure can be found in the efforts of the Idaho National Laboratory and collaborators researching resilient control systems. This effort takes a holistic approach to next generation design, and considers the resilience aspects that are not well quantified, such as cyber security,[22] human interaction and complex interdependencies.

Another example is MIT's ongoing CarTel project where a fleet of taxis work by collecting real-time traffic information in the Boston area. Together with historical data, this information is then used for calculating fastest routes for a given time of the day.[23]

CPS are also used in electric grids to perform advanced control, especially in the smart grids context to enhance the integration of distributed renewable generation.The Special remedial action scheme are needed to limit the current flows in the grid when wind farm generation is too high. Distributed CPS are a key solution for this type of issues [24]

In industry the cyber–physical systems empowered by Cloud technologies have led to novel approaches[25][26][27] that paved the path to Industry 4.0 as the European Commission IMC-AESOP project with partners such as Schneider Electric, SAP, Honeywell, Microsoft etc. demonstrated.

Design

Файл:CPS for Manufacturing.png

A challenge in the development of embedded and cyber–physical systems is the large differences in the design practice between the various engineering disciplines involved, such as software and mechanical engineering. Additionally, as of today there is no "language" in terms of design practice that is common to all the involved disciplines in CPS. Today, in a marketplace where rapid innovation is assumed to be essential, engineers from all disciplines need to be able to explore system designs collaboratively, allocating responsibilities to software and physical elements, and analyzing trade-offs between them. Recent advances show that coupling disciplines by using co-simulation will allow disciplines to cooperate without enforcing new tools or design methods.[28] Results from the MODELISAR project show that this approach is viable by proposing a new standard for co-simulation in the form of the Functional Mock-up Interface.

Importance

The US National Science Foundation (NSF) has identified cyber–physical systems as a key area of research.[29] Starting in late 2006, the NSF and other United States federal agencies sponsored several workshops on cyber–physical systems.[30][31][32][33][34][35][36][37][38]

See also

References

Шаблон:Reflist

Further reading

External links

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  1. ↑ Шаблон:Cite journal
  2. ↑ "US National Science Foundation, Cyber-Physical Systems (CPS)"
  3. ↑ 3,0 3,1 Hu, J.; Lennox, B.; Arvin, F., "Robust formation control for networked robotic systems using Negative Imaginary dynamics" Automatica, 2022.
  4. ↑ 4,0 4,1 4,2 4,3 4,4 Patil T., Rebaioli L., Fassi I., "Cyber-physical systems for end-of-life management of printed circuit boards and mechatronics products in home automation: A review" Sustainable Materials and Technologies, 2022.
  5. ↑ 5,0 5,1 Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F., "Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring" Aerospace Science and Technology, 2022.
  6. ↑ Шаблон:Cite journal
  7. ↑ Suh, S.C., Carbone, J.N., Eroglu, A.E.: Applied Cyber-Physical Systems. Springer, 2014.
  8. ↑ Шаблон:Cite journal
  9. ↑ 9,0 9,1 Khaitan et al., "Design Techniques and Applications of Cyber Physical Systems: A Survey", IEEE Systems Journal, 2014.
  10. ↑ C.Alippi: Intelligence for Embedded Systems. Springer Verlag, 2014, 283pp, Шаблон:ISBN.
  11. ↑ Шаблон:Cite web
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  14. ↑ J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, and J. Landay, "UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits," in Proceedings of the 27th international conference on Human factors in computing systems. ACM, 2009, pp. 1043–1052.
  15. ↑ P. Handel, I. Skog, J. Wahlstrom, F. Bonawide, R. Welsh, J. Ohlsson, and M. Ohlsson: Insurance telematics: opportunities and challenges with the smartphone solution, Intelligent Transportation Systems Magazine, IEEE, vol.6, no.4, pp. 57-70, winter 2014, Шаблон:Doi
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  20. ↑ S. Karnouskos: Cyber-Physical Systems in the Smart Grid (PDF; 79 kB). In:Industrial Informatics (INDIN), 2011 9th IEEE International Conference on, July 2011. Retrieved 20 Apr 2014.
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  25. ↑ A. W. Colombo, T. Bangemann, S. Karnouskos, J. Delsing, P. Stluka, R. Harrison, F. Jammes, and J. Lastra: Industrial Cloud-based Cyber- Physical Systems: The IMC-AESOP Approach. Springer Verlag, 2014, Шаблон:ISBN.
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  27. ↑ Wu, D., Rosen, D.W., & Schaefer, D. (2014). Cloud-Based Design and Manufacturing: Status and Promise. In: Schaefer, D. (Ed): Cloud-Based Design and Manufacturing: A Service-Oriented Product Development Paradigm for the 21st Century, Springer, London, UK, pp.1-24.
  28. ↑ J .Fitzgerald, P.G. Larsen, M. Verhoef (Eds.): Collaborative Design for Embedded Systems: Co-modelling and Co-simulation. Springer Verlag, 2014, Шаблон:ISBN.
  29. ↑ Шаблон:Cite journal
  30. ↑ Шаблон:Cite web
  31. ↑ Шаблон:Cite web
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  33. ↑ Шаблон:Cite web
  34. ↑ Шаблон:Cite web
  35. ↑ Шаблон:Cite web
  36. ↑ Шаблон:Cite web
  37. ↑ Шаблон:Cite web
  38. ↑ Шаблон:Cite web
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