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Intelligent Fuzzy Q-Learning Control of Humanoid Robots

  • Meng Joo Er
  • Yi Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

In this paper, a design methodology for enhancing the stability of humanoid robots is presented. Fuzzy Q-Learning (FQL) is applied to improve the Zero Moment Point (ZMP) performance by intelligent control of the trunk of a humanoid robot. With the fuzzy evaluation signal and the neural networks of FQL, biped robots are dynamically balanced in situations of uneven terrains. At the mean time, expert knowledge can be embedded to reduce the training time. Simulation studies show that the FQL controller is able to improve the stability as the actual ZMP trajectories become close to the ideal case.

Keywords

Humanoid Robot Biped Robot Zero Moment Point Negative Small Positive Medium 
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 2005

Authors and Affiliations

  • Meng Joo Er
    • 1
  • Yi Zhou
    • 1
  1. 1.Intelligent Systems CenterSingapore

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