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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 to effectively control a dynamically stable system. We formulate the learning problem as a support vector regression and develop a new SVM learning structure to better implement human control strategy learning in control. The approach is fundamentally valuable in dealing with problems that normally dynamically stable robots experience, such as small sample data and local minima, and therefore is extremely useful in abstracting human controller for dynamic systems. The experimental study on the SVM approach with respect to other approaches clearly demonstrated the superiority of the SVM approach in terms of fidelity, efficiency and effectiveness in implementation.