Model reference learning approach and its applications to robot impedance control

Abstract

In this paper, a model reference learning control (MRLC) law is proposed for a class of nonlinear and time varying system. The robustness of the MRLC system to dynamics fluctuations, output measurement noises and errors in initial conditions is analyzed. The design method for analyzing the MRLC system is developed and sufficient conditions for the convergence are derived. An application of the MRLC to robotic impedance control is addressed and an impedance learning control (ImpLC) approach is presented.