Disturbance estimation and modeling by iterative learning process for performance improvement in trajectory control

This paper presents a modeling methodology for unknown disturbances based oil a disturbance estimation using a iterative learning process in mechatronic systems. In this reserach, the nonlinear friction and file modeling errors between mathematical model and actual plant system should be handled as the disturbances in mechanism because these phenomenons mainly deteriorate the trajectory control performances. The friction call be mathematically modeled by using the learned estimation, as a function of displacement, velocity, acceleration, and jerk of file actuator. This model has a distinguished feature that the friction compensation can be achieved with 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 oil machine stand.