Order Iterative Learning Control Research Articles

DISTURBANCE ASPECTS OF HIGH ORDER ITERATIVE LEARNING CONTROL

Some initial results on the disturbance properties of high order iterative learning control (ILC) algorithms are presented. Of particular interest is to investigate how high order ILC algorithms cope with measurement and load disturbances of different character. Some results are obtained by assuming statistical properties of the involved disturbances and deriving equations for the covariance matrix of the control error vector. The results are illustrated by analytic derivation of the covariance matrix for a second order ILC algorithm with a particular choice of design variables.

ON MONOTONIC CONVERGENCE OF HIGH ORDER ITERATIVE LEARNING UPDATE LAWS

High-order iterative learning control (ILC) in both iteration domain and time domain is investigated in this paper. We are interested in whether a high order iterative learning updating law is helpful in achieving a monotonic convergence in a suitable norm topology other than the exponentially weighted sup-norm. Discrete-time linear time invariant system is considered. With simulation illustrations, it is shown that a high-order scheme in both time domain and iteration domain is helpful. A new design framework for high order ILC is proposed.

A Model Based ILC Method Applied to a Commercial Industrial Robot

A model based design method for the filters in a first order Iterative Learning Control (ILC) algorithm is presented and evaluated on a commercial industrial robot. Using the measurements of motor position and reference position, the error on the motor-side of the robot is minimized using ILC. The result is evaluated both on the motor-side as well as the arm-side. It is shown that the error also on the arm-side can be reduced dramatically. In the experiments it is shown that for this particular robot 5-6 iterations is enough (including a modeling experiment) to reach a steady state level of the error.