# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
## 1 Introduction
[PERSON] divided the gradient surface of the machined parts into a group of homogeneous trajectory groups [17], with final ILC control data of the first trajectory acting as reference. Then, those ILC control data are transformed via proportional scale and offset, which are used as the initial data of the next adjacent trajectory iterative learning. [PERSON] expressed the new desired trajectory as combinations of serial former trajectories, and combined the control data obtained from the former ILC as the initial iteration data of the new desired trajectory [18]. [PERSON] proposed an ILC algorithm for optimal matching of trajectories [19]. The new desired trajectory is segmented, translated and rotated to obtain the primitive combined trajectory. Through the series combination of trajectory, control data and time-scale transformation, the initial iteration control data are obtained. [PERSON] used the control data of symmetric trajectories for iterative learning control via mirror transformation [20]. [PERSON] constructed an initial iterative control data set containing multi-frequency components of the system [21]. He also introduced an upper limit to the initial data for the input signal of ILC to avoid the aggressive response due to the uncertainty that lies in high frequencies. Besides, the robust iterative learning model predictive control algorithm with variable reference trajectory proposed by [PERSON] also aimed at solving the control experience inheritance problem of ILC when the desired trajectory changes [22]. These studies used the pre-adjusted experience data from former ILC for a new desired trajectory. As the convergence of the error depends highly on the initial choice of input applied to the ILC, a set of good initial data would make learning faster and, as a consequence, the error tends to the required accuracy faster as well. All the above studies only consider the ILC experience inheritance in the situations where the desired trajectory changes in the new control task. However, there are some other neglected situations which need ILC experience inheritance. For example, a new motor with higher power substitutes the old one in the robot manipulator driving unit, or some other parameters like the load, the damp ratio changes in the system. Until now, no research involving the ILC experience inheritance in such situations has been reported. In modern industry, the changes of working process occur frequently. As the structure and essence of the system remain unchanged, so it is possible to transfer the former control experience or knowledge into the new system. For such systems, according to the specific relation within the parameters change, the inner relevance of two systems, the experience obtained from the former ILC might be inherited and transformed as the initial iterative control data of the new iterative learning process, which could save the time and resource in the new system. The paper is organized as follows. In the second section, the traditional ILC algorithm is introduced. Its limitations are described. In the third section, an ILC of recombining transform strategy is presented. In the fourth section, an ILC of translational adjusting strategy is introduced. In the fifth section, an ILC of amplitude adjusting strategy is introduced. The simulation results are given to illustrate its merits over the traditional ILC in each section. Next, the convergence property of the ILC with experience transfer is analyzed. The conclusion is drawn in the final section.