On stability region analysis for a class of human learning controllers

In this paper, we study the stability region for a set of intelligent controllers developed by learning human expert control skills using support vector machines (SVMs). Based on the discrete-time system Lyapunov theory, a Chebychev points based estimation approach is proposed to evaluate the stability region, a key property of this set of SVM-based human learning controllers. One of such learning controllers has been implemented in vertical balance control of a dynamically stable, statically unstable single wheel mobile robot - Gyrover. The experimental results validate the proposed scheme for estimation of the stability region.