Support Vector Machine Based Approach for Abstracting Human Control Strategy in Controlling Dynamically Stable Robots

Authors:
Yongsheng Ou;Huihuan Qian;Yangsheng Xu
Affiliations:
Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, USA 18015;Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, China;Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, China
Venue:
Journal of Intelligent and Robotic Systems
Year:
2009

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Abstract

In this paper, we discuss the problem of how human control strategy can be represented as a parametric model using a support vector machine (SVM) and how an SVM-based controller can be used in controlling dynamically stable systems. The SVM approach has been implemented in the balance control of Gyrover, which is a dynamically stable, statically unstable, single-wheel mobile robot. The experimental results that compare SVM with general artificial neural network approaches clearly demonstrate the superiority of the SVM approach with regard to human control strategy learning.