Initial Value Researching of Iterative Learning and Its Application in Robot

Abstract

In iterative learning conrtol,a common assumption is that the initial state of each iterative is equal to its idea value.This assumption is very important to the stability analysis for the system,to the iterative learning conrtol,ghe distrubance of initial state will directly affect the precision of trajectory tracking.In this paper,a new iterative learning control scheme is presented,in which the forgetting factor is used make down the affection of the initial state disturbance,and the initial state is updated while the convergence of system is guaranteed.Under this interative learning conrtol scheme the initial state and tracking of system will convergence to their idea value.At last,the simulation results for robot system illustrate the effectiveness of the new iterative learning control scheme presented in this paper.