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

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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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Projects 60974047 and U1134004, by the Natural Science Foundation of Guangdong Province under Grant S2012010008967, by the Science Fund for Distinguished Young Scholars (S20120011437), by the 2011 Zhujiang New Star, by the FOK Ying Tung Education Foundation of China under Grant 121061, by the Ministry of Education of New Century Excellent Talent, by the 973 Program of China under Grant 2011CB013104, and by the Doctoral Fund of Ministry of Education of China under Grant 20124420130001.

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Authors and Affiliations

  1. Faculty of Automation, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China

    Liyang Wang, Zhi Liu & Yun Zhang

  2. Department of Electronic Engineering, Shunde Polytechnic, Foshan, Guangdong, 528300, China

    Liyang Wang

  3. Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira, S.J., Taipa, Macau, S.A.R., P.R. China

    C. L. Philip Chen

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  1. Liyang Wang
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  4. Yun Zhang
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Correspondence to Liyang Wang.

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Wang, L., Liu, Z., Chen, C.L.P. et al. Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking. Appl Intell 40, 453–463 (2014). https://doi.org/10.1007/s10489-013-0472-2

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