Robust feedback error learning method for controller design of nonlinear systems

This work presents a new robust controller design method for nonlinear system based on feedback error learning (FEL) method and higher order derivatives of universal learning networks (ULNs). Our idea is to make an inverse model robust to signal noise by adding the sensitivity terms to the standard criterion function. Through feedback error learning, the sensitivity term can be minimized as well as usual criterion functions using the higher order derivatives of ULNs. As a result, it is confirmed by using simulation results that NNC robust against signal noise can be obtained.