Support Vector Regression for Controller Approximation

Recently, the support vector machine (SVM) that is a new learning methodology based on Vapnik Chervonenkis (VC) theory is proposed. With introducing Vapnik's epsiv-insensitive loss function, the SVM has been extended to solve a nonlinear regression estimation problem, called the support vector regression (SVR), which has been shown to exhibit excellent performance. Due to its good properties, the SVR are successfully applied to the various applications. In this paper, the SVR applies to approximate controller in the inverted pendulum system. That is, the controller can be reconstructed by the proposed method. Hence, the proposed controller is a new controller that can replace original controller. Additionally, the selection of parameters of SVR are also discussed and analysis for this application. Simulation results are provided to show the validity and applicability of the proposed approach.