Advertisement

A PLC-Based System for Advanced Control

  • Samo Gerkšič
  • Gregor Dolanc
  • Damir Vrančić
  • Juš Kocijan
  • Stanko Strmčnik
  • Sašo Blažič
  • Igor Škrjanc
  • Zoran Marinšek
  • Miha Božiček
  • Anna Stathaki
  • Robert King
  • Mincho Hadjiski
  • Kosta Boshnakov
Part of the Advances in Industrial Control book series (AIC)

Abstract

The chapter presents a PLC-based system for advanced control called ASPECT. The ASPECT controller was designed to be an efficient and user-friendly engineering tool for the implementation of parameter-scheduling nonlinear control in the process industry, which is achieved by partial automation of the commissioning procedure. The key to the concept is the self-tuning mechanism. The controller parameters are automatically tuned from a nonlinear process model. The model is determined on the basis of operating process signals by experimental modelling, where an online-learning procedure is used. This procedure is based on model identification using the local learning approach. The two main components of the ASPECT system are the Run-time Module (RTM) and the Configuration Tool (CT). The RTM runs on a PLC or an embedded controller, performing all the main functionality of real-time control, online learning, and control performance monitoring. The CT, used on a personal computer (PC) only during the initial configuration phase, simplifies the commissioning procedure by providing guidance and default parameter values. The performance of the system is demonstrated with simulation experiments on a pH control process and with experimental application to an industrial valve-testing apparatus. In the conclusion, the lessons learned during the development and implementation of the system are discussed.

Keywords

Local Model Controller Parameter Local Controller Model Branch Configuration Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The contributions of all other partners in the ASPECT project are gratefully acknowledged. The ASPECT project was financially supported by the EC under contract IST-1999-56407. ASPECT ©2002 software is the property of INEA d.o.o., Indelec Europe S.A., and Start Engineering JSCo. This chapter is based on: Gerkšič S. et al. (2006) Advanced control algorithms embedded in a programmable logic controller, Control Engineering Practice, 14:935–948 ©Elsevier.

References

  1. 1.
    Anderson BDO, Brinsmead T, Liberzon D, Morse AS (2001) Multiple model adaptive control with safe switching. Int J Adapt Control Signal Process 15:445–470 zbMATHCrossRefGoogle Scholar
  2. 2.
    Angelov PP, Filev DP (2004) An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Trans Syst Man Cybern, Part B, Cybern 34(1):484–497 CrossRefGoogle Scholar
  3. 3.
    Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning. ISA, International Society for Measurement and Control, Raleigh Google Scholar
  4. 4.
    Babuska R, Oosterhoff J, Oudshoorn A, Bruijn PM (2002) Fuzzy self-tuning PI control of pH in fermentation. Eng Appl Artif Intell 15:3–15 CrossRefGoogle Scholar
  5. 5.
    Bequette BW (1991) Non-linear control of chemical processes: a review. Ind Eng Chem Res 30:1391–1413 CrossRefGoogle Scholar
  6. 6.
    Blažič S, Škrjanc I, Gerkšič S, Dolanc G, Strmčnik S, Hadjiski MB, Stathaki A (2003) On-line fuzzy identification for an advanced intelligent controller. In: Proceedings IEEE international conference on industrial technology ICIT 2003, Maribor, Slovenia, pp 912–916 Google Scholar
  7. 7.
    Blažič S, Škrjanc I, Gerkšič S, Dolanc G, Strmčnik S, Hadjiski MB, Stathaki A (2009) Online fuzzy identification for an intelligent controller based on a simple platform. Eng Appl Artif Intell 22:628–638 CrossRefGoogle Scholar
  8. 8.
    Frantz FK (1995) A taxonomy of model abstraction techniques. In: Proceedings of the 1995 winter simulation conference, Arlington, VA, USA, pp 1413–1420 CrossRefGoogle Scholar
  9. 9.
    Dougherty D, Cooper D (2003) A practical multiple model adaptive strategy for multivariable model predictive control. Control Eng Pract 11:649–664 CrossRefGoogle Scholar
  10. 10.
    Dovžan D, Škrjanc I (2011) Recursive clustering based on a Gustafson–Kessel algorithm. Evol Syst 2:15–24 CrossRefGoogle Scholar
  11. 11.
    Dahleh M, Rinehart M (2009) Networked decision systems. In: Samad T, Annaswamy A (eds) The impact of control technology. IEEE control systems society report. Online: http://ieeecss.org/main/IoCT-report Google Scholar
  12. 12.
    Gerkšič S, Dolanc G, Vrančić D, Kocijan J, Strmčnik S, Blažič S, Škrjanc I, Marinšek Z, Božiček M, Stathaki A, King R, Hadjiski M, Boshnakov K (2006) Advanced control algorithms embedded in a programmable logic controller. Control Eng Pract 14:935–948 CrossRefGoogle Scholar
  13. 13.
    Gundala R, Hoo KA, Piovoso MJ (2000) Multiple model adaptive control design for a multiple-input multiple-output chemical reactor. Ind Eng Chem Res 39:1554–1564 CrossRefGoogle Scholar
  14. 14.
    Hägglund T, Åström KJ (2000) Supervision of adaptive control algorithms. Automatica 36:1171–1180 zbMATHCrossRefGoogle Scholar
  15. 15.
    Henson MA, Seborg DE (1994) Adaptive non-linear control of a pH neutralisation process. IEEE Trans Control Syst Technol 2:169–182 CrossRefGoogle Scholar
  16. 16.
    Henson MA, Seborg DE (1997) Non-linear process control. Prentice-Hall PTR, Upper Saddle River Google Scholar
  17. 17.
    INEA d.o.o. (2001) IDR BLOK process control tools for Mitsubishi Electric PLC’s. User’s Manual, Ver. 4.20. INEA d.o.o., Ljubljana, http://www.inea.si/index.php?kategorija=158
  18. 18.
    Isermann R (1991) Digital control systems: stochastic control, multivariable control, adaptive control, applications, vol 2, 2nd revised edn. Springer, Berlin Google Scholar
  19. 19.
    Kocijan J, Žunič G, Strmčnik S, Vrančić D (2002) Fuzzy gain-scheduling control of a gas+liquid separation plant implemented on a PLC. Int J Control 75(14):1082–1091 zbMATHCrossRefGoogle Scholar
  20. 20.
    Kocijan J, Vrančić D, Dolanc G, Gerkšič S, Strmčnik S, Škrjanc I, Blažič S, Božiček M, Marinšek Z, Hadjinski MB, Boshnakov K, Stathaki A, King R (2003) Auto-tuning non-linear controller for industrial use. In: Proc of IEEE international conference on industrial technology ICIT 2003, Maribor, Slovenia, pp 906–910 CrossRefGoogle Scholar
  21. 21.
    King RE, Koumboulis FN, Stathaki A (2002) Intelligent hybrid industrial control. In: Proceedings of 1st intl IEEE symp intelligent systems, Varna, September 2002, vol 2, pp 2–6 CrossRefGoogle Scholar
  22. 22.
    Koumboulis FN, King RE, Stathaki A (2007) Logic-based switching controllers—a stepwise safe switching approach. Inf Sci 177(13):2736–2755 zbMATHCrossRefGoogle Scholar
  23. 23.
    Leith DJ, Leithead WE (1998) Appropriate realisation of MIMO gain-scheduled controllers. Int J Control 70(1):13–50 MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Ljung L (1987) System identification. Prentice Hall, Englewood Cliffs zbMATHGoogle Scholar
  25. 25.
    Maciejowski JM (2002) Predictive control with constraints. Prentice Hall, Harlow Google Scholar
  26. 26.
    Morse AS (1995) Control using logic-based switching. In: Isidori A (ed) Trends in control—a European perspective. Springer, London, pp 69–113 CrossRefGoogle Scholar
  27. 27.
    Murray-Smith R, Johansen TA (eds) (1997) Multiple model approaches to modelling and control. Taylor and Francis, London Google Scholar
  28. 28.
    Nelles O, Fink A, Isermann R (2000) Local linear model trees (lolimot) toolbox for nonlinear system identification. In: IFAC symposium on system identification (SYSID), Santa Barbara, USA Google Scholar
  29. 29.
    Qin SJ, Badgwell TA (1999) An overview of nonlinear model predictive control applications. In: Allgöwer F, Zheng A (eds) Nonlinear model predictive control. Birkhäuser, Basel, pp 370–392 Google Scholar
  30. 30.
    Richalet J (1993) Pratique de la commande prédictive. Hermès, Paris Google Scholar
  31. 31.
    Samad T, Annaswamy A (eds) (2011) The impact of control technology. IEEE Control Systems Society report. Online: http://ieeecss.org/main/IoCT-report
  32. 32.
    Seborg DE (1999) A perspective on advanced strategies for process control (revisited). In: Frank PM (ed) Advances in control, highlights of ECC’99. Springer, London, pp 103–134 CrossRefGoogle Scholar
  33. 33.
    Smith OJM (1959) A controller to overcome dead-time. ISA J 6(2):28–33 Google Scholar
  34. 34.
    Stephanopoulus G, Henning G, Leone H (1990) Model LA—a modelling language for process engineering, II. Multifaceted modelling of processing systems. Comput Chem Eng 14:847–869 CrossRefGoogle Scholar
  35. 35.
    Takatsu H, Itoh T, Araki M (1998) Future needs for the control theory in industries—report and topics of the control technology survey in Japanese industry. J Process Control 8(5/6):369–374 CrossRefGoogle Scholar
  36. 36.
    Tan S, Hang C-C, Chai J-S (1997) Gain scheduling: from conventional to neuro-fuzzy. Automatica 33(3):411–419 MathSciNetzbMATHCrossRefGoogle Scholar
  37. 37.
    Vrančić D, Huba M (2007) LEK tuner—program package for tuning PID controllers. In: Mikleš J, Fikar M, Kvasnica M (eds) Proceedings of 16th international conference process control 2007, Štrbské Pleso. Slovak University of Technology, Bratislava, pp 225-1–225-5 Google Scholar
  38. 38.
    Vrančić D, Strmčnik S, Juričić D (2001) A magnitude optimum multiple integration tuning method for filtered PID controller. Automatica 37:1473–1479 zbMATHCrossRefGoogle Scholar
  39. 39.
    Whiteley AL (1946) Theory of servo systems, with particular reference to stabilization. J IEE, Part II 93(34):353–372 Google Scholar
  40. 40.
    Wooldridge M, Jennings NR (1995) Intelligent agents, theory and practice. Knowl Eng Rev 10(2):115–152 CrossRefGoogle Scholar
  41. 41.
    Zeigler PB (1979) Structuring principles for multifaceted system modelling. In: Zeigler BP, Elzas MS, Klir GJ, Ören TI (eds) Methodology in systems modelling and simulation. North-Holland, Amsterdam, pp 93–135 Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Samo Gerkšič
    • 1
  • Gregor Dolanc
    • 1
  • Damir Vrančić
    • 1
  • Juš Kocijan
    • 1
    • 2
  • Stanko Strmčnik
    • 1
  • Sašo Blažič
    • 3
  • Igor Škrjanc
    • 3
  • Zoran Marinšek
    • 4
  • Miha Božiček
    • 5
  • Anna Stathaki
    • 6
  • Robert King
    • 7
  • Mincho Hadjiski
    • 8
  • Kosta Boshnakov
    • 8
  1. 1.Department of Systems and ControlJožef Stefan InstituteLjubljanaSlovenia
  2. 2.University of Nova GoricaNova GoricaSlovenia
  3. 3.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  4. 4.INEA d.o.o.LjubljanaSlovenia
  5. 5.TSmedia d.o.o.LjubljanaSlovenia
  6. 6.Computer Technology InstituteAthensGreece
  7. 7.School of EngineeringUniversity of PatrasPatrasGreece
  8. 8.University of Chemical Technology and Metallurgy-SofiaSofiaBulgaria

Personalised recommendations