Skip to main content
SpringerLink
Log in

Control Architecture for Automation

  • Chapter
  • First Online:
Springer Handbook of Automation

Part of the book series: Springer Handbooks ((SHB))

  • 5315 Accesses

Abstract

Automation technology has always tried to ensure efficient reusability of technology building blocks and effective implementation of solutions with modular approaches. These approaches, also known as control architectures for automation, are experiencing an increasing change toward more software-based methods. All classical architectures, such as NC, CNC, and PLC, are currently reaching their limits in terms of flexibility, adaptability, connectivity, and expandability. In addition, more and more functionalities are shifting from the classic pyramid-shaped communication to the smallest embedded devices or even to systems on higher levels of the communication pyramid such as MES. Operation technology is more and more penetrated by IT, which, due to its high speed of innovation, opens up ever-growing solution spaces. These include convergent networks that are equipped with real-time functionality and can be used directly in production for all kinds of horizontal and vertical communication needs. New wireless communications such as Wi-Fi 6 and 5G also represent a current revolution in automation technology – however, this does not apply in consumer electronics. Therefore, it is time to sketch new control architectures for automation. The advantage of these new architectures can not only be found in the classical application area of production but also in engineering in general. For example, the method of hardware-in-the-loop simulation has considerable potential in production control as well as in the upfront simulation of production plants and virtual commissioning. As in many other fields of applications, the topic of artificial intelligence is added as a further architectural component, as well. AI recently became a very versatile tool to solve a broad range of problems at different hierarchical levels of the automation pyramid.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 309.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 399.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arnold, H.: The recent history of the machine tool industry and the effects of technological change. Münchner Betriebswirtschaftliche Beiträge, vol. 14 (2001)

    Google Scholar 

  2. Brecher, C., Verl, A., Lechler, A., Servos, M.: Open control systems: state of the art. Prod. Eng. Res. Dev. 4(2-3), 247–254 (2010). https://doi.org/10.1007/s11740-010-0218-5

    Article  Google Scholar 

  3. PLC Open: Function Blocks for Motion Control: Part 1. [Online]. Available: https://plcopen.org/system/files/downloads/plcopen_motion_control_part_1_version_2.0.pdf. Accessed 17 May 2020

  4. Tan, K.K., Lee, T.H., Huang, S.: Precision Motion Control: Design and Implementation. Springer, London (2007)

    Google Scholar 

  5. Suh, S.-H., Kang, S.-K., Chung, D.-H., Stroud, I.: Theory and design of CNC, Springer Science and Business Media (2008)

    Google Scholar 

  6. Keinert, M., Kaiser, B., Lechler, A., Verl, A.: Analysis of CNC software modules regarding parallelization capability. In: Proceedings of the 24th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), San Antonio (2014)

    Google Scholar 

  7. OPC UA for Machine Tools: Part 1 – Monitoring and Job, 40501-1, OPC Foundation, Sept (2020)

    Google Scholar 

  8. OPC Unified Architecture for Robotics: Part 1: Vertical Integration, OPC 40010-1, OPC Foundation, May (2019)

    Google Scholar 

  9. OPC UA for Machinery: Part 1 – Basic Building Blocks, 40001-1, OPC Foundation, Sept (2020)

    Google Scholar 

  10. Cordis: European Commission:CORDIS:Projects and Results: Open System Architecture for Controls within Automation Systems. [Online]. Available: https://cordis.europa.eu/project/id/1783. Accessed 25 Sept 2020

  11. Brecher, C., Verl, A., Lechler, A., Servos, M.: Open control systems: state of the art. Prod. Eng. Res. Dev. 4(2–3), 247–254. https://doi.org/10.1007/s11740-010-0218-5

  12. Plattform Industrie 4.0: Details of the Administration Shell – Part 1. [Online]. Available: https://www.dex.siemens.com/mindsphere/mindsphere-packages. Accessed 26 Sept 2020

  13. AutomationML e.V.: AutomationML. [Online]. Available: https://www.automationml.org/o.red.c/home.html. Accessed 26 Sept 2020

  14. eCl@ss e.V.: eCl@ss Content. [Online]. Available: https://www.eclasscontent.com/index.php. Accessed 26 Sept 2020

  15. Siemens Industry Software Inc.: MindSphere Packages. [Online]. Available: https://www.dex.siemens.com/mindsphere/mindsphere-packages. Accessed 26 Sept 2020

  16. Schlechtendahl, J., Keinert, M., Kretschmer, F., Lechler, A., Verl, A.: Making existing production systems industry 4.0-ready. Prod. Eng. Res. Dev. 9(1), 143–148 (2015)

    Article  Google Scholar 

  17. Industrial automation systems and integration. Physical device control. Data model for computerized numerical controllers, 14649, ISO, Apr 2004

    Google Scholar 

  18. 3S-Smart Software Solutions GmbH, CODESYS® Runtime: Converting any intelligent device into an IEC 61131-3 controller using the CODESYS Control Runtime System

    Google Scholar 

  19. Levis, A.H.: Systems architecture. In: Handbook of Systems Engineering, 2nd edn, pp. 479–506. Wiley (2009)

    Google Scholar 

  20. Buede, D.M., Miller, W.D.: The Engineering Design of Systems: Models and Methods. Wiley, Hoboken, (2016)

    Google Scholar 

  21. Moghaddam, M., Cadavid, M.N., Kenley, C.R., Deshmukh, A.V.: Reference architectures for smart manufacturing: a critical review. J. Manuf. Syst. 49, 215–225 (2018). https://doi.org/10.1016/j.jmsy.2018.10.006

    Article  Google Scholar 

  22. Morel, G., Pereira, C.E., Nof, S.Y.: Historical survey and emerging challenges of manufacturing automation modeling and control: a systems architecting perspective. Annu. Rev. Control. 47, 21–34 (2019). https://doi.org/10.1016/j.arcontrol.2019.01.002

    Article  Google Scholar 

  23. katacontainers: Kata Containers Docs: Understand the basics, contribute to and try using Kata Containers. [Online]. Available: https://katacontainers.io/docs/. Accessed 21 Apr 2020

  24. Google LLC: gVisor Documentation. [Online]. Available: https://gvisor.dev/docs/. Accessed 22 Apr 2020

  25. Abeni, L., Biondi, A., Bini, E.: Hierarchical scheduling of real-time tasks over Linux-based virtual machines. J. Syst. Softw. 149, 234–249 (2019). https://doi.org/10.1016/j.jss.2018.12.008

    Article  Google Scholar 

  26. Bini, E., Bertogna, M., Baruah, S.: Virtual multiprocessor platforms: specification and use. In: 30th IEEE Real-Time Systems Symposium, 2009: RTSS 2009; 1–4 Dec. 2009, Washington, DC, USA; proceedings, Washington, DC, pp. 437–446 (2009)

    Google Scholar 

  27. Shin, I., Easwaran, A., Lee, I.: Hierarchical scheduling framework for virtual clustering of multiprocessors. In: Euromicro Conference on Real-Time Systems, 2008: ECRTS ‘08; 2–4 July 2008, Prague, Czech Republic, Prague, pp. 181–190 (2008)

    Google Scholar 

  28. Friebel, T., Biemueller, S.: How to Deal with Lock Holder Preemption, Xen Summit North America 164 (2008)

    Google Scholar 

  29. Ramsauer, R., Kiszka, J., Lohmann, D., Mauerer, W.: Look mum, no VM exits! (almost). In: Proceedings of the 13th Workshop on Operating Systems Platforms for Embedded Real-Time Applications (OSPERT). [Online]. Available: https://arxiv.org/pdf/1705.06932

  30. Schlechtendahl, J., Kretschmer, F., Lechler, A., Verl, A.: Communication Mechanisms for Cloud Based Machine Controls, Procedia CIRP 17, pp. 830–834 (2014). https://doi.org/10.1016/J.PROCIR.2014.01.074

  31. Schlechtendahl, J., Kretschmer, F., Sang, Z., Lechler, A., Xu, X.: Extended study of network capability for cloud based control systems. Robot. Comput. Integr. Manuf. 43, 89–95 (2017). https://doi.org/10.1016/j.rcim.2015.10.012

    Article  Google Scholar 

  32. Vonasek, V., Pěnička, R., Vick, A., Krüger, J.: Robot control as a service – towards cloud-based motion planning and control for industrial robots, In 10th International Workshop on Robot Motion and Control (RoMoCo), pp. 33–39 (2015)

    Google Scholar 

  33. Vick, A., Horn, C., Rudorfer, M., Krüger, J.: Control of robots and machine tools with an extended factory cloud. In: IEEE International Workshop on Factory Communication Systems – Proceedings, WFCS, vol. 2015 (2015). https://doi.org/10.1109/WFCS.2015.7160575

  34. Kretschmer, F., Friedl, S., Lechler, A., Verl, A.: Communication extension for cloud-based machine control of simulated robot processes. In 2016 IEEE International Conference on Industrial Technology (ICIT), pp. 54–58 (2016)

    Google Scholar 

  35. Masmano, M., Ripoll, I., Crespo, A., Jean-Jacques, M.: XtratuM: A Hypervisor for Safety Critical Embedded Systems, In 11th Real-Time Linux Workshop, pp. 263–272 (2009)

    Google Scholar 

  36. Xi, S., Wilson, J., Lu, C., Gill, C.: RT-Xen: towards real-time hypervisor scheduling in Xen. In: 2011 Proceedings of the Ninth ACM International Conference on Embedded Software (EMSOFT), pp. 39–48 (2011)

    Google Scholar 

  37. Sandstrom, K., Vulgarakis, A., Lindgren, M., Nolte, T.: Virtualization technologies in embedded real-time systems, In IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–8 (2013)

    Google Scholar 

  38. Auer, M.E., Zutin, D.G. (eds.): Online engineering & Internet of Things: Proceedings of the 14th International Conference on Remote Engineering and Virtual Instrumentation REV 2017, held 15-17 March 2017, Columbia University, New York, USA. Springer International Publishing, Cham (2018)

    Google Scholar 

  39. Givehchi, O., Imtiaz, J., Trsek, H., Jasperneite, J.: Control-as-a-service from the cloud: a case study for using virtualized PLCs. In: 2014 10th IEEE Workshop on Factory Communication Systems (WFCS 2014), pp. 1–4 (2014)

    Google Scholar 

  40. Goldschmidt, T., Murugaiah, M.K., Sonntag, C., Schlich, B., Biallas, S., Weber, P.: Cloud-based control: a multi-tenant, horizontally scalable soft-PLC. In: 2015 IEEE 8th International Conference on Cloud Computing, pp. 909–916 (2015)

    Google Scholar 

  41. Garcia, C.A., Garcia, M.V., Irisarri, E., Pérez, F., Marcos, M., Estevez, E.: Flexible container platform architecture for industrial robot control. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1056–1059 (2018)

    Google Scholar 

  42. Nikolakis, N., Senington, R., Sipsas, K., Syberfeldt, A., Makris, S.: On a containerized approach for the dynamic planning and control of a cyber – physical production system. Robot. Comput. Integr. Manuf. 64, 101919 (2020). https://doi.org/10.1016/j.rcim.2019.101919

    Article  Google Scholar 

  43. Telschig, K., Schönberger, A., Knapp, A.: A real-time container architecture for dependable distributed embedded applications. In: 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), pp. 1367–1374 (2018)

    Google Scholar 

  44. Morabito, R.: Virtualization on internet of things edge devices with container technologies: a performance evaluation. IEEE Access. 5, 8835–8850 (2017). https://doi.org/10.1109/ACCESS.2017.2704444

    Article  Google Scholar 

  45. Goldschmidt, T., Hauck-Stattelmann, S.: Software containers for industrial control. In: 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 258–265 (2016)

    Google Scholar 

  46. Tasci, T., Walker, M., Lechler, A., Verl, A.: Towards modular and distributed automation systems: towards modular and distributed automation systems: leveraging software containers for industrial control. In: Automation2020 (2020)

    Google Scholar 

  47. Tasci, T., Melcher, J., Verl, A.: A container-based architecture for real-time control applications. In: Conference proceedings, ICE/IEEE ITMC 2018: 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC): Stuttgart 17.06.–20.06.18, Stuttgart, pp. 1–9 (2018)

    Google Scholar 

  48. Faller, C., Höftmann, M.: Service-oriented communication model for cyber-physical-production-systems. Procedia CIRP. 67, 156–161 (2018). https://doi.org/10.1016/j.procir.2017.12.192

    Article  Google Scholar 

  49. Lo Bello, L., Steiner, W.: A perspective on IEEE time-sensitive networking for industrial communication and automation systems. Proc. IEEE. 107(6), 1094–1120 (2019). https://doi.org/10.1109/JPROC.2019.2905334

    Article  Google Scholar 

  50. Cisco Systems: IEEE 802.11ax: The Sixth Generation of Wi-Fi White Paper. [Online]. Available: https://www.cisco.com/c/en/us/products/collateral/wireless/white-paper-c11-740788.html

  51. Unified Architecture Part 14: PubSub, OPC Foundation, Feb 2018. [Online]. Available: https://opcfoundation.org/developer-tools/specifications-unified-architecture/part-14-pubsub/

  52. OPC UA for Plastics and Rubber Machinery: Extrusion Lines and Their Components, 40084, OPC Foundation, Apr 2020

    Google Scholar 

  53. OPC UA for Plastics and Rubber Machinery: Data exchange Between Injection Moulding Machines and MES, 40077, OPC Foundation, Apr 2020

    Google Scholar 

  54. OPC UA for Plastics and Rubber Machinery: Peripheral devices – Part 1: Temperature Control Devices, 40082-1, OPC Foundation, Apr 2020

    Google Scholar 

  55. OPC UA for Plastics and Rubber Machinery: General Type Definitions, 40083, OPC Foundation, Apr 2020

    Google Scholar 

  56. umati Initiative: umati – universal machine technology interface. [Online]. Available: https://www.umati.org/about/. Accessed 26 Sept 2020

  57. PROFIBUS Nutzerorganisation e.V.: PROFINET over TSN: Guideline, 1st ed. [Online]. Available: https://www.profibus.com/download/profinet-over-tsn/

  58. Weber, K.: EtherCAT and TSN – Best Practices for Industrial Ethernet System Architectures. Available: https://www.ethercat.org/download/documents/Whitepaper_EtherCAT_and_TSN.pdf. Accessed 1 Apr 2020

  59. Szancer, S., Meyer, P., Korf, F.: Migration from SERCOS III to TSN-simulation based comparison of TDMA and CBS transportation. In: OMNeT++, pp. 52–62 (2018)

    Google Scholar 

  60. Bruckner, D., et al.: OPC UA TSN: A New Solution for Industrial Communication. Available: https://www.intel.com/content/dam/www/programmable/us/en/pdfs/literature/wp/opc-ua-tsn-solution-industrial-comm-wp.pdf. Accessed 9 Apr 2020

  61. OPC Foundation: Initiative: Field Level Communications (FLC): OPC Foundation extends OPC UA including TSN down to field level. Available: https://opcfoundation.org/flc-pdf. Accessed 24 Apr 2020

  62. Fricke, E., Gebhard, B., Negele, H., Igenbergs, E.: Coping with changes: causes, findings, and strategies. Syst. Eng. 3(4), 169–179 (2000). https://doi.org/10.1002/1520-6858(2000)3:4<169::AID-SYS1>3.0.CO;2-W

    Article  Google Scholar 

  63. Röck, S.: Hardware in the loop simulation of production systems dynamics. Prod. Eng. Res. Dev. 5(3), 329–337 (2011). https://doi.org/10.1007/s11740-011-0302-5

    Article  Google Scholar 

  64. Virtual commissioning: model types and glossary, 3693 Part 1, Verein Deutscher Ingenieure e.V. (VDI); Verband der Elektrotechnik Elektronik Informationstechnik e.V. (VDE), Berlin, Aug 2016

    Google Scholar 

  65. Pritschow, G., Röck, S.: “Hardware in the loop” simulation of machine tools. CIRP Ann. Manuf. Technol. 53(1), 295–298 (2004). https://doi.org/10.1016/S0007-8506(07)60701-X

    Article  Google Scholar 

  66. Software and systems engineering part 2: software testing: test processes, 29119-2, International Organization for Standardization (ISO); International Electrotechnical Commission (IEC); Institute of Electrical and Electronics Engineers (IEEE), Geneva, s.l., New York, Sept 2013

    Google Scholar 

  67. Kübler, K., Neyrinck, A., Schlechtendahl, J., Lechler, A., Verl, A.: Approach for manufacturer independent automated machine tool control software test. In: Wulfsberg, J.P. (ed.) Applied Mechanics and Materials, v.794, WGP Congress 2015: Progress in production engineering; selected, peer reviewed papers from the 2015 WGP Congress, September 7–8, 2015, Hamburg, Germany, Pfaffikon: Trans Tech Publications Inc, pp. 347–354 (2015)

    Google Scholar 

  68. Kormann, B., Tikhonov, D., Vogel-Heuser, B.: Automated PLC software testing using adapted UML sequence diagrams. IFAC Proc. Vol. 45(6), 1615–1621 (2012). https://doi.org/10.3182/20120523-3-RO-2023.00148

    Article  Google Scholar 

  69. Kormann, B., Vogel-Heuser, B.: Automated test case generation approach for PLC control software exception handling using fault injection. In: Proceedings of 2011 IECON, Melbourne, pp. 365–372 (2011)

    Google Scholar 

  70. Kübler, K., Schwarz, E., Verl, A.: Test case generation for production systems with model-implemented fault injection consideration. Procedia CIRP. 79, 268–273 (2019). https://doi.org/10.1016/j.procir.2019.02.065

    Article  Google Scholar 

  71. Enoiu, E.P., Čaušević, A., Ostrand, T.J., Weyuker, E.J., Sundmark, D., Pettersson, P.: Automated test generation using model checking: an industrial evaluation. Int. J. Softw. Tools Technol. Transfer. 18(3), 335–353 (2016). https://doi.org/10.1007/s10009-014-0355-9

    Article  Google Scholar 

  72. Rösch, S., Tikhonov, D., Schütz, D., Vogel-Heuser, B.: Model-based testing of PLC software: test of plants’ reliability by using fault injection on component level. IFAC Proc. Vol. 47(3), 3509–3515 (2014). https://doi.org/10.3182/20140824-6-ZA-1003.01238

    Article  Google Scholar 

  73. Wiener, N., Mitter, S., Hill, D.: Cybernetics or Control and Communication in the Animal and the Machine, Reissue of the 1961, 2nd edn. The MIT Press, S.l., Cambridge, Massachusetts (2019)

    Google Scholar 

  74. Kuipers, B., Feigenbaum, E.A., Hart, P.E., Nilsson, N.J.: Shakey: from conception to history. AIMag. 38(1), 88 (2017). https://doi.org/10.1609/aimag.v38i1.2716

    Article  Google Scholar 

  75. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge, MA (2018)

    MATH  Google Scholar 

  76. Tunyasuvunakool, S., et al.: dm_control: software and tasks for continuous control. Softw. Impacts. 6, 100022 (2020). https://doi.org/10.1016/j.simpa.2020.100022

    Article  Google Scholar 

  77. Ross, S., et al.: Learning monocular reactive UAV control in cluttered natural environments. In: 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, May 2013, pp. 1765–1772 (2013)

    Google Scholar 

  78. Andrychowicz, M., et al.: Hindsight Experience Replay, July 2017. [Online]. Available: http://arxiv.org/pdf/1707.01495v3

  79. Deisenroth, M.P., Rasmussen, C.E.: PILCO: a model-based and data-efficient approach to policy search. In: 28th International Conference on Machine Learningg, ICML, pp. 465–473 (2011)

    Google Scholar 

  80. Mahler, J., et al.: Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics, Mar 2017. Available: http://arxiv.org/pdf/1703.09312v3

  81. Mahler, J., Goldberg, K.: Learning deep policies for robot bin picking by simulating robust grasping sequences. In: Conference on Robot Learning, pp. 515–524 (2017)

    Google Scholar 

  82. Shin, H.-C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging. 35(5), 1285–1298 (2016). https://doi.org/10.1109/TMI.2016.2528162

    Article  Google Scholar 

  83. Csiszar, A., Eilers, J., Verl, A.: On solving the inverse kinematics problem using neural networks. In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland, Nov 2017, pp. 1–6 (2017)

    Google Scholar 

  84. Huang, S.N., Tan, K.K., Lee, T.H.: Adaptive friction compensation using neural network approximations. IEEE Trans. Syst. Man Cybern. C. 30(4), 551–557 (2000). https://doi.org/10.1109/5326.897081

    Article  Google Scholar 

  85. Ghallab, M., Nau, D.S., Traverso, P.: Automated Planning and Acting. Cambridge University Press, New York (2016). [Online]. Available: https://www.loc.gov/catdir/enhancements/fy1618/2016017697-b.html

    Book  MATH  Google Scholar 

  86. Helo, P., Suorsa, M., Hao, Y., Anussornnitisarn, P.: Toward a cloud-based manufacturing execution system for distributed manufacturing. Comput. Ind. 65(4), 646–656 (2014). https://doi.org/10.1016/j.compind.2014.01.015

    Article  Google Scholar 

  87. Dornhege, C., Eyerich, P., Keller, T., Trueg, S., Brenner, M., Nebel, B.: Semantic attachments for domain-independent planning systems. In: Towards Service Robots for Everyday Environments

    Google Scholar 

  88. Mirza, M., et al.: Physically Embedded Planning Problems: New Challenges for Reinforcement Learning, Sept 2020. Available: http://arxiv.org/pdf/2009.05524v1

  89. Lepenioti, K., Bousdekis, A., Apostolou, D., Mentzas, G.: Prescriptive analytics: literature review and research challenges. Int. J. Inf. Manag. 50, 57–70 (2020). https://doi.org/10.1016/j.ijinfomgt.2019.04.003

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the help of all colleagues involved in this chapter and especially the contributors who could not be listed as authors: Dr. Akos Csiszar, Caren Dripke, Florian Frick, Karl Kübler, and Timur Tasci. Without their support and their specific content, this chapter would not have become a reality. Also, many thanks to the sponsors (German Research Foundation, Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy) of various projects through which the results could be generated.

Author information

Authors and Affiliations

  1. Institute for Control Engineering of Machine Tools and Manufacturing Unit, University of Stuttgart, Stuttgart, Germany

    Oliver Riedel, Armin Lechler & Alexander W. Verl

Authors
  1. Oliver Riedel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Armin Lechler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander W. Verl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oliver Riedel .

Editor information

Editors and Affiliations

  1. PRISM Center and School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Shimon Y. Nof

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Riedel, O., Lechler, A., Verl, A.W. (2023). Control Architecture for Automation. In: Nof, S.Y. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-96729-1_16

Download citation

Publish with us

Policies and ethics

Search

Navigation