An adaptive control model of a locomotion by the central pattern generator

  • Jun Nishii
Computational Models of Neurons and Neural Nets
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)


Most of basic locomotor patterns of living bodies are controlled by central pattern generators (CPGs) which are collective neural oscillators. The CPG sends control signals to muscular systems, and the activity of the CPG is strongly affected by sensory signals from the body. Therefore, it can be said that locomotor patterns are generated by the interaction between the CPGs and the body movements. To control a physical system, such as a leg, by the CPG, it would be necessary to obtain an adequate intrinsic frequency of the CPG and interactions between the CPG and the physical system. In this article, we regard a physical system as a physical oscillator and propose a learning algorithm to acquire these parameters and apply the learning method to the control of a hopping robot. The proposed learning method does not need any information about the dynamics of the controlled physical system and requires only local informations like the Hebbian rule.


Physical System Learning Rule Central Pattern Generator Locomotor Pattern Intrinsic Frequency 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Jun Nishii
    • 1
  1. 1.Department of Mathematical Engineering and Information Physics Faculty of EngineeringUniversity of TokyoTokyoJapan

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