Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking

  • Authors:

  • Liyang Wang;Zhi Liu;C. L. Chen;Yun Zhang

  • Affiliations:

  • Faculty of Automation, Guangdong University of Technology, Guangzhou, China 510006 and Department of Electronic Engineering, Shunde Polytechnic, Foshan, China 528300;Faculty of Automation, Guangdong University of Technology, Guangzhou, China 510006;Faculty of Science and Technology, University of Macau, Taipa, P.R. China;Faculty of Automation, Guangdong University of Technology, Guangzhou, China 510006

  • Venue:
  • Applied Intelligence
  • Year:
  • 2014

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Abstract

An interval type-2 fuzzy weighted support vector machine (IT2FW-SVM) is proposed to address the problem of high energy consumption for biped walking robots. Different from the traditional machine learning method of `copy learning', the proposed IT2FW-SVM obtains lower energy cost and larger zero moment point (ZMP) stability margin using a novel strategy of `selective learning', which is similar to human selections based on experience. To handle the uncertainty of the experience, the learning weights in the IT2FW-SVM are deduced using an interval type-2 fuzzy logic system (IT2FLS), which is an extension of the previous weighted SVM. Simulation studies show that the existing biped walking which generates the original walking samples is improved remarkably in terms of both energy efficiency and biped dynamic balance using the proposed IT2FW-SVM.