Skip to main content

Advertisement

Log in

Disturbance management design for a holonic multiagent manufacturing system by using hybrid approach

Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper proposes a disturbance management methodology for an agent-based holonic manufacturing system by using hybrid control approach, with its application to a mobile manipulator system. Firstly, the architectures based on holarchy and agents are given. Then the framework of hybrid control strategy is outlined and the stability analysis for the closed-loop system is introduced. The major contribution of this work is the exploration for using hybrid control approach into disturbance management mechanism. The switched disturbance detector is presented and the identification algorithm is given. The hybrid controller is designed to reject the disturbance by switching between reactions against diagnosed symptoms. Finally, the case study onto the mobile manipulator hybrid system verifies the effectiveness and applicability of this design method. It validates the agility, efficiency, and retaining stability to system of the holonic multiagent concept for industrial or other application systems.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Koestler A (1989) The ghost in the machine. Arkana Books, London

    Google Scholar 

  2. Zhang X, Balasubramanian S, Brennan RW, Norrie DH (2000) Design and implementation of a real-time holonic control system for manufacturing. Inf Sci 127(1–2):23–44

    Article  Google Scholar 

  3. Ulieru M, Geras A (2002) Emergent holarchies for e-health applications: a case in glaucoma diagnosis. Proc IEEE 4(5–8):2957–2961

    Google Scholar 

  4. Fletcher M, Deen SM (2001) Fault-tolerant holonic manufacturing systems. Concurr Comput, Pract Exp 13(1):43–70

    Article  MATH  Google Scholar 

  5. Gou L, Luh PB, Kyoya Y (1998) Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation. Comput Ind 37(3):213–231

    Article  Google Scholar 

  6. Leitão P, Restivo F (2008) Implementation of a holonic control system in a flexible manufacturing system. IEEE Trans Syst Man Cybern, Part C, Appl Rev 38(5):699–709

    Article  Google Scholar 

  7. Leitão P (2004) An agile and adaptive holonic architecture for manufacturing control. PhD dissertation, University of Porto, Porto, Portugal

  8. Frizelle G, McFarlane D, Bongaerts L (1998) Disturbance measurement in manufacturing production systems. In: Proceedings of the ASI-98 of network of excellence in intelligent control and integrated manufacturing systems, Bremen, Germany, pp 159–162

    Google Scholar 

  9. Benaskeur A, Irandoust H (2008) Holonic approach for control and coordination of distributed sensors. Technical report, DRDC Valcartier TR 2008-015, Defence R&D Canada-Valcartier, Canada

  10. Fierro R, Lewis FL (1997) A framework for hybrid control design. IEEE Trans Syst Man Cybern, Part A, Syst Hum 27(6):765–773

    Article  Google Scholar 

  11. Heragu SS, Graves RJ, Kim B-I, Onge ASt (2002) Intelligent agent based framework for manufacturing systems control. IEEE Trans Syst Man Cybern, Part A, Syst Hum 32(5):560–573

    Article  Google Scholar 

  12. Passino KM, Özgüner Ü (1991) Modeling and analysis of hybrid systems: examples. In: Proceedings of IEEE international symposium on intelligent control, Arlington, VA, pp 251–256

    Google Scholar 

  13. Puri A, Varaiya P (1995) Modeling and verification of hybrid systems. In: Proceedings of American control conference, Seattle, WA, pp 4466–4470

    Google Scholar 

  14. McClintock J, Fierro R (2008) A hybrid system approach to formation reconfiguration in cluttered environments. In: Proceedings of 16th Mediterranean conference on control and automation, Ajaccio, France, pp 83–88

    Google Scholar 

  15. Valckenaers P (1998) Integration of scheduling and control in holonic manufacturing systems. PhD dissertation, Katholieke University Leuven, Heverlee, Belgium

  16. Giebels M (2008) EtoPlan: a concept for concurrent manufacturing planning and control. Thesis, University of Twente Publications, Enschede, The Netherlands

  17. Zhang X, Norrie DH (1999) Holonic control at the production and controller levels. In: Proceedings of the 2nd international workshop on intelligent manufacturing systems, pp 215–224

    Google Scholar 

  18. McFarlane DC, Bussmann S (2000) Developments in holonic production planning and control. Int J Prod Plann Control 11(6):522–536

    Article  Google Scholar 

  19. Bussmann S (1998) An agent-oriented architecture for holonic manufacturing control. In: Proceedings of first international workshop on IMS, Lausanne, Switzerland, pp 1–12

    Google Scholar 

  20. Shen W, Xue D, Norrie DH (1998) An agent-based manufacturing enterprise infrastructure for distributed integrated intelligent manufacturing systems. In: Proceedings of the 3rd international conference on the practical application of intelligent agents and multi-agents, London, UK, pp 533–548

    Google Scholar 

  21. Leitão P, Restivo F (2000) A framework for distributed manufacturing applications. In: Proceedings of the 2000 advanced summer institute, pp 1–7

    Google Scholar 

  22. Su C, Li H (2012) An affective learning agent with Petri-net-based implementation. Appl Intell. doi:10.1007/s10489-012-0350-3

    Google Scholar 

  23. Gao J, Lv H (2011) Institution-governed cross-domain agent service cooperation: a model for trusted and autonomic service cooperation. Appl Intell. doi:10.1007/s10489-011-0323-y

    Google Scholar 

  24. O’Shea K (2012) An approach to conversational agent design using semantic sentence similarity. Appl Intell. doi:10.1007/s10489-012-0349-9

    Google Scholar 

  25. Treur J (2011) A virtual human agent model with behaviour based on feeling exhaustion. Appl Intell 35(3):469–482

    Article  Google Scholar 

  26. Bosse T, Gerritsen C, Treur J (2011) Combining rational and biological factors in virtual agent decision making. Appl Intell 34(1):87–101

    Article  Google Scholar 

  27. Both F, Hoogendoorn M, van der Mee A, Treur J, de Vos M (2012) An intelligent agent model with awareness of workflow progress. Appl Intell 36(2):498–510

    Article  Google Scholar 

  28. Ye H, Michel AN, Huo L (1995) Stability theory for hybrid dynamical systems. In: Proceedings of IEEE conference on decision control, New Orleans, LA, pp 2679–2684

    Google Scholar 

  29. Lei J (2007) Research on optimal disturbance rejection methods for systems with control delay. ME dissertation, Ocean University of China, Qingdao, China (in Chinese)

  30. Lei J (2010) Study on optimal vibration control for time-delay systems with application to vehicle suspension systems. DE dissertation, Ocean University of China, Qingdao, China (in Chinese)

  31. Isidori A (2005) Nonlinear control systems. Publishing House of Electronics Industry, Beijing (in Chinese)

    Google Scholar 

  32. Lei J (2012) Output feedback suboptimal disturbance rejection for linear systems: internal-model principle method. J Yunnan Univ, Nat Sci Edition 34(4):408–414 (in Chinese)

    Google Scholar 

  33. Lei J (2011) Suboptimal vibration control for nonlinear suspension systems based on in-vehicle networks. In: Proceedings of 2011 international conference on system science and engineering, Macau, China, pp 239–244

    Chapter  Google Scholar 

  34. Flah A, Sbita L (2012) A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive. Appl Intell. doi:10.1007/s10489-012-0361-0

    Google Scholar 

  35. Rebarber R, Weiss G (2003) Internal model based tracking and disturbance rejection for stable well-posed systems. Automatica 39(9):1555–1569

    Article  MathSciNet  MATH  Google Scholar 

  36. Ding Z (2003) Univeral disturbance rejection for nonlinear systems in output feedback form. IEEE Trans Autom Control 48(7):1222–1227

    Article  Google Scholar 

  37. Bodson M (2001) Performance of an adaptive algorithm for sinusoidal disturbance rejection in high noise. Automatica 37(7):1133–1140

    Article  MATH  Google Scholar 

  38. Branicky MS (1994) Stability of switched and hybrid systems. In: Proceedings of conference on decision and control, Lake Buena Vista, FL, pp 3498–3503

    Google Scholar 

  39. Branicky MS (1998) Multiple Lyapunov functions and other analysis tools for switched and hybrid systems. IEEE Trans Autom Control 43(4):475–482

    Article  MathSciNet  MATH  Google Scholar 

  40. Lin H, Antsaklis PJ (2008) Stability and stabilizability of switched linear systems: a survey of recent results. In: Proceedings of ISIC-MED joint conference, pp 24–29

    Google Scholar 

Download references

Acknowledgements

The author is grateful to the reviewers whose comments helped to significantly improve the presentation of the results in this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Lei.

Additional information

This work was supported in part by the Natural Science Foundation of Yunnan Province (2011FZ169), the Open Fund of Key Laboratory in Software Engineering of Yunnan Province (2011SE15), and the Scientific Research Foundation for Talented Scholars of Yunnan Nationalities University.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lei, J., Yang, ZY. Disturbance management design for a holonic multiagent manufacturing system by using hybrid approach. Appl Intell 38, 267–278 (2013). https://doi.org/10.1007/s10489-012-0369-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-012-0369-5

Keywords

Navigation