Convergence analysis for a class of skill learning controllers

This paper studied convergence conditions for a class of intelligent controllers. We formulated conditions to verify that the learned closed-form control system is strongly stable under perturbations (SSUP). We developed an approach to evaluate the convergence quality of this class of controllers with representation of support vector machine. It has been implemented in a balance control of a dynamically stable, statically unstable single wheel robot. The experimental results verified the proposed convergence conditions and the theory upon which it is based.