Model of Neurocontrol of Anthropomorphic Systems

  • A. Frolov
  • S. Řízek
  • M. Dufossé
Conference paper


The proposed neural model of control of multijoint anthropomorphic systems imitates the visual-motor transformations performed in living creatures. It involves three subtasks: development of the model of the human arm biomechanics; modelling of the neuromuscular apparatus of living creatures; design of the central neurocontroller. The Equilibrium Point theory simplifies the task (reaching movement) performed by the central neurocontroller to the inverse static problem. The contribution of various nonlinear effects of muscle force generation on the accuracy of linear approximation have been tested and qualified. It has been found that the presence of the time delay is substantial. The proposed complex model may provide a scientific base for the design of anthropomorphic robots and manipulators.


Muscle Force Intrafusal Fibre Inverse Dynamic Problem Anthropomorphic Robot Equilibrium Point Hypothesis 
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  1. [1]
    Feldman A.G.: Once More on the EquilibriumPoint Hypothesis (⋋ Model) for Motor Control. J. Motor. Behav., 18, 1986, 17–54.CrossRefGoogle Scholar
  2. [2]
    Frolov A., Řízek S.: Differential Neurocontrol of Multidimensional Systems. In: Dealing with Complexity: A Neural Network Approach. (ed. Karny M., Warwick K., Kürkova V.), Springer, London, 1998, 238–251.Google Scholar
  3. [3]
    Frolov A., Dufossé M., Řízek S., Kaladjian A.: On the Possibility of Linear Modelling the Arm Neuromuscular Apparatus. Biological Cybernetics, 82, 2000, 449–515.CrossRefGoogle Scholar
  4. [4]
    Gomi H., Kawato M.: Human Arm Stiffness and Equilibrium-Point Trajectory during Multi-Joint Movement. Biol. Cyber., 76, 1997, 163–171.CrossRefMATHGoogle Scholar
  5. [5]
    Řízek S., Frolov A.A.: Inftuence of feedback upon learning of the differential neurocontroller. Neural Network World, 3, 1996, 347–353.Google Scholar
  6. [6]
    Gribble P.L., Ostry D.J., Sanguineti V., Laboissiere R.: Are Complex Control Signals Required for Human Arm Movement? J. Neurophysiol. 79, 1998, 1409–1424.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • A. Frolov
    • 1
  • S. Řízek
    • 2
  • M. Dufossé
    • 3
  1. 1.Inst. of Higher Nervous Activity and Neurophys.Russian Acad. of Sci.MoscowRussia
  2. 2.Inst. of Computer ScienceAcad. of Sci. of the Czech RepublicPrague 8Czech Republic
  3. 3.INSERM U483Univ. P. M. CurieParisFrance

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