Modeling, Analysis and Control of Mechanoreceptors with Adaptive Features

  • Carsten BehnEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 325)


This work—the development of new control strategies and sensor models—is motivated by the open question which occurred during analysis of the functional morphology of vibrissal sensor systems. The reception of vibrations is a special sense of touch, important for many insects and vertebrates. The latter realize this reception by means of hair-shaped vibrissae, to acquire tactile information about their environments. The vibrissa receptors are in a permanent state of adaption to filter the perception of tactile stimuli. This behavior now may be mimicked by an artificial sensor system. The sensor system is modeled as a spring-mass-damper system with relative degree two and the system parameters are supposed to be unknown, due to the complexity of biological systems. Using a simple linear model of a sensory system adaptive controllers are considered which compensate unknown permanent ground excitations. The working principle of each controller (feedback law including adaptor) is shown in numerical simulations which prove that these controllers in fact work successfully and effectively. Moreover, practical implementation of these controllers to a demonstrator in form of an electrical oscillating circuit results in various successful experiments which confirm the theoretical results.


Adaptive control Bio-inspired sensor system Modeling Uncertain system 



The author thanks Joachim Steigenberger (TU Ilmenau) for his critical remarks, improving suggestions and his continued interest in my work. Thanks go also to Johannes Zeh, a former diploma student of the author, who made up a fairly long list of adaptors from literature. The successfully accomplished experiments with an electrical device are done with the assistance of him, Valter Böhm and Siegfried Oberthür (all from TU Ilmenau).


  1. 1.
    Barth, F.G.: Spider mechanoreceptors. Curr Opin Neurobiol 14, 415–422 (2004)Google Scholar
  2. 2.
    Behn, C.: Adaptive control of straight worms without derivative measurement. Multibody Sys Dyn 26(3), 213–243 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Behn, C.: Mathematical modeling and control of biologically inspired uncertain motion systems with adaptive features. Habilitation thesis, Ilmenau University of Ilmenau, Germany (2013)Google Scholar
  4. 4.
    Behn, C., Steigenberger, J.: Improved adaptive controllers for sensory systems - First Attempts. In: Modeling, Simulation and Control of Nonlinear Engineering Dynamical Systems, (Ed.) J. Awrejcewicz, pp. 161–178. Springer, Amsterdam (2009)Google Scholar
  5. 5.
    Behn, C., Steigenberger, J.: Experiments in adaptive control of uncertain mechanical systems. Int Rev Mech Eng 4(7), 886–898 (2010)Google Scholar
  6. 6.
    Behn, C., Zimmermann, K.: Adaptive \(\lambda \) - tracking For locomotion systems. Robot Auton Syst 54, 529–545 (2006)CrossRefGoogle Scholar
  7. 7.
    Dudel, J., Menzel, R., Schmidt, R.F.: Neurowissenschaft. Springer, Berlin (1996)Google Scholar
  8. 8.
    Ebara, S., Kumamoto, K., Matsuura, T., Mazurkiewicz, J.E., Rice, F.L.: Similarities and differences in the innervation of mystacial vibrissal follicle-sinus complexes in the rat and cat: A confocal microscopic study. J Comp Neurol 449, 103–119 (2002)CrossRefGoogle Scholar
  9. 9.
    Georgieva, P., Ilchmann, A.: Adaptive \(\lambda \)-tracking control of activated sludge processes. Int J Control 74(12), 1247–1259 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Ilchmann, A.: Non-identifier-based adaptive control of dynamical systems: a survey. IMA J Math Control Inform 8, 321–366 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Iwasaki, M., Itoh, T., Tominaga, Y.: VI Mechano- and Phonoreceptors. In: Atlas of arthropod sensory receptors - Dynamic Morphology in Relation to Function, (Eds.): E. Eguchi and Y. Tominaga, pp. 177–190. Springer, Berlin (1999)Google Scholar
  12. 12.
    Miller, D.E., Davison, E.J.: An adaptive controller which provides an arbitrarily good transient and steady-state response. IEEE Trans Autom Control 36, 68–81 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Ogata, K.: Modern control engineering. 3rd Edition, Prentice Hall (1997)Google Scholar
  14. 14.
    Rice, F.L., Mance, A., Munher, B.L.: A comparative light microscopic analysis of the sensory innervation of the mystacial pad. I. Innervation of vibrissal follicle-sinus complexes. J Comp Neurol 252, 154–174 (1986)CrossRefGoogle Scholar
  15. 15.
    Smith, C.U.M.: Biology of Sensory Systems. 2nd Edition. Wiley, New York (2008)Google Scholar
  16. 16.
    Soderquist, D.R.: Sensory processes. Sage Publications, Thousand oaks (2002)Google Scholar
  17. 17.
    Sontag, E.D.: Mathematical control theory. 2nd Edition, Springer, Berlin (1998)Google Scholar
  18. 18.
    Voges, D., Carl, K., Klauer, G., Uhlig, R., Behn, C., Schilling, C., Witte, H.: Structural characterisation of the whisker system of the rat. IEEE Sens J 12(2), 332–339 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringIlmenau University of TechnologyIlmenauGermany

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