A Novel Bio-Inspired Approach for Adaptive Manufacturing System Control

  • Wenbin Gu
  • Dunbing TangEmail author
  • Lei Wang
Conference paper
Part of the Advanced Concurrent Engineering book series (ACENG)


The trend towards a global market and the increasing customer orientation impel the manufacturing discipline to seek new paradigms. As biological organisms are quite capable of adapting to environmental changes and stimulus, bio-inspired concepts have been recognized much suitable for adaptive manufacturing system control. This paper, therefore, proposes a novel concept of NeuroEndocrine-Inspired Manufacturing System (NEIMS). The proposed NEIMS control architecture is inherited from neuro-control and hormone regulation principles to agilely deal with the frequent occurrence of unexpected disturbances at the shop floor level. From the cybernetics point of view, the control model of NEIMS has been described in detail. And a test bed has been set up to enable the NEIMS simulation.


Adaptive manufacturing system control Bio-inspired manufacturing system Neuroendocrine system Neuro-control Hormone regulation 



This paper is supported by Fok Ying Tung Education Foundation (No. 111056), and the Research Fund for Doctoral Program of Higher Education, China (No. 20093218110020), and the New Century Excellent Talents in University, China (NCET-08).


  1. 1.
    Ueda K, Vaario J, Ohkura K. Modeling of biological manufacturing systems for dynamic reconfiguration. Ann CIRP. 1997;46:343–6.CrossRefGoogle Scholar
  2. 2.
    Brennan RW, Fletcher M, Norrie DH. An agent-based approach to reconfiguration of real-time distributed control systems. IEEE Trans Robot Autom. 2002;18(4):444–51.CrossRefGoogle Scholar
  3. 3.
    Ryu K, Jung M. Agent-based fractal architecture and modeling for developing distributed manufacturing systems. Int J Prod Res. 2003;41(17):4233–55.CrossRefGoogle Scholar
  4. 4.
    Brussel H, Van Wyns J, Valckenaers P, Bongaerts L, Peeters P. Reference architecture for holonic manufacturing systems: PROSA. Comput Ind. 1998;37(3):255–74.CrossRefGoogle Scholar
  5. 5.
    Farhy LS. Modeling of oscillations of endocrine networks with feedback. Methods Enzymol. 2004;384:54–81.CrossRefGoogle Scholar
  6. 6.
    Keenan DM, Licinio J, Veldhuis JD. A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary adrenal axis. PNAS. 2001;98(7):4028–33.CrossRefGoogle Scholar
  7. 7.
    Xiang W, Lee HP. Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng Appl Artif Intell. 2008;21:73–85.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

Personalised recommendations