Fast and precise positioning by sequential adaptive feedforward compensation for disturbance

Abstract

This paper presents a fast and precise positioning of table systems using a sequential adaptive methodology for disturbance. In this research, both nonlinear friction and a modeling error between mathematical model and actual plant system are handled as disturbances in mechanism. It is well-known that disturbance variations deteriorate positioning performance. Viscous friction and a motor thrust constant are taken up a problem as primary factors in disturbance variations, because those parameters are frequently varied for temperature change due to drive conditions, such as before/after warming up motion. In this research, feedforward compensation using a disturbance model is applied. Disturbance model parameters are genetically optimized by GA to simulate actual disturbance characteristics, where faithful disturbance characteristics are obtained using an iterative learning process. A sequential adaptive methodology is tuned the model parameters continuously to achieve robust positioning performance irrespective of temperature change. The proposed approach with the adaptive disturbance model-based feedforward compensation has been verified by experiments using a table system on a machine stand.