Adaptive Friction Compensation of Servo System Based on Iterative Learning

An adaptive friction compensation approach based on iterative learning is developed in this note to address a class of nonlinear servo systems with repeatable actions and time variant friction model uncertainties, which are iteration independent. By using the composite energy function which provides the system information along time and iterative horizons, and through a mathematically rigorous analysis, the algorithm is proved to be stable convergence along the iterative learning horizon, and the tracking error is asymptotic convergence as the iteration number increased. The simulation illustrations are given to show the approach can achieve high-accuracy trajectory control and it is feasible to practical applications.