Disturbance Estimation and Modeling by Iterative Learning Process for Performance Improvement in Trajectory Control

This paper presents a modeling methodology for unknown disturbances based on a disturbance estimation using a iterative learning process in mechatronic systems. In this research, the nonlinear friction and the modeling errors between mathematical model and actual plant system should be handled as the disturbances in mechanism because these phenomena mainly deteriorate the trajectory control performances. The friction can be mathematically modeled by using the learned estimation, as a function of displacement, velocity, acceleration, and jerk of the actuator. This model has a distinguished feature that the friction compensation can be achieved with a generalization capability for different conditions. The proposed positioning control approach with the disturbance modeling and compensation has been verified by experiments using a table drive system on machine stand.