Advertisement

Phase Agents and Dynamic Routing for Batch Process Automation

  • Wilfried Lepuschitz
  • Benjamin Groessing
  • Emilian Axinia
  • Munir Merdan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8062)

Abstract

Currently applied process automation solutions rely on predefined control recipes with preprogrammed material transfer routes in the subjacent control software. Thus, the flexibility is limited with regard to dynamic environment conditions such as a change of production job priorities or a modification of the system layout. In this context, agent technology is seen as a promising approach for providing such flexibility. This paper presents a multi-agent system for batch process automation, which introduces the concept of phase agents for controlling the physical equipment. The phase agents incorporate control software based on the standard IEC 61131 for PLC programming in consideration of compliance to the standard IEC 61512 Batch Control. In the context of material transfers, a route finding algorithm is introduced for dynamically calculating suitable routes. Moreover, demonstration applications are presented to show the feasibility of the approach.

Keywords

Agent technology batch process flexible automation dynamic routing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Guo, Q.L., Zhang, M.: An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing. Robotics and Computer-Integrated Manufacturing 26(1), 39–45 (2010)CrossRefGoogle Scholar
  2. 2.
    Mes, M., van der Heijden, M., van Harten, A.: Comparison of agent-based scheduling to look-ahead heuristics for real-time transportation problems. European Journal of Operational Research 181(1), 59–75 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Brennan, R.: Toward Real-Time distributed intelligent control: A survey of research themes and applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(5), 744–765 (2007)CrossRefGoogle Scholar
  4. 4.
    Rajakumar, S., Arunachalam, V., Selladurai, V.: Workflow balancing strategies in parallel machine scheduling. The International Journal of Advanced Manufacturing Technology 23, 366–374 (2004)CrossRefGoogle Scholar
  5. 5.
    Wahab, M., Stoyan, S.: A dynamic approach to measure machine and routing flexibilities of manufacturing systems. International Journal of Production Economics 113(2), 895–913 (2008)CrossRefGoogle Scholar
  6. 6.
    Vrba, P., Mařík, V.: Capabilities of dynamic reconfiguration of Multiagent-Based industrial control systems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(2), 213–223 (2010)CrossRefGoogle Scholar
  7. 7.
    Merdan, M., Lepuschitz, W., Axinia, E.: Advanced Process Automation Using Automation Agents. In: Proceedings of the 5th International Conference on Automation, Robotics and Applications, pp. 34–39 (2011)Google Scholar
  8. 8.
    Jämsä-Jounela, S.L.: Future trends in process automation. Annual Reviews in Control 31(2), 211–220 (2007)CrossRefGoogle Scholar
  9. 9.
    Leitao, P.: Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence 22(7), 979–991 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Jennings, N., Bussmann, S.: Agent-based control systems: Why are they suited to engineering complex systems? IEEE Control Systems Magazine 23(3), 61–73 (2003)CrossRefGoogle Scholar
  11. 11.
    Metzger, M., Polaków, G.: A survey on applications of agent technology in industrial process control. IEEE Transactions on Industrial Informatics 7(4), 570–581 (2011)CrossRefGoogle Scholar
  12. 12.
    International Electrotechnical Commission: IEC 61131 Programmable controllers – Part 3: Programming languages, Geneva (1993)Google Scholar
  13. 13.
    International Electrotechnical Commission: IEC 61512 Batch Control – Part 1: Models and Terminology, Geneva (1997)Google Scholar
  14. 14.
    Parker, M., Rawtani, J.: Practical Batch Process Management. Newnes, The Netherlands (2005)Google Scholar
  15. 15.
    Kuikka, S.: A batch process management framework: Domain-specific, design pattern and software component based approach. PhD thesis, Helsinki University of Technology, Espoo (1999)Google Scholar
  16. 16.
    Dijkstra, E.W.: A Note on Two Problems in Connexion with Graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Telecom Italia: Jade - Java Agent Development Framework (access date March 2013)Google Scholar
  18. 18.
    Rockwell Automation: CompactLogix Control Systems (access date March 2013)Google Scholar
  19. 19.
    COPA-DATA: HMI SCADA Software zenon by COPA-DATA (access date March 2013)Google Scholar
  20. 20.
    OPC Foundation: OPC Unified Architecture (access date March 2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wilfried Lepuschitz
    • 1
  • Benjamin Groessing
    • 1
  • Emilian Axinia
    • 2
  • Munir Merdan
    • 1
  1. 1.Automation and Control InstituteVienna University of TechnologyViennaAustria
  2. 2.COPA-DATA GmbHSalzburgAustria

Personalised recommendations