Non‐linear system identification and control based on neural and self‐tuning control

This paper deals with identification and control of non‐linear systems that contain linear parameters and a non‐linear quasi‐input function of the real input signal. Least squares have been used to estimate the linear parametric part, while a neural feedforward net is employed to estimate the remaining non‐linearity. Performance considerations are illustrated by hybrid algorithm simulations.