Analysis And Application Of Iterative Learning Control

Iterative learning control (ILC) is a new addition to the control techniques. It can improve the tracking performance and transient response of control systems which performs the same tasks in a finite time interval. According to the research actuality, this dissertation studied problems on convergence, robustness, initial state and the application of iterative learning control. The main contents and results in this dissertation are as follows.1. The convergence conditions of the basic ILC algorithm are given. The convergence and robustness of open-close D-type,P-type and open PID-type learning law are proved.2. Studied the convergence of ILC based on the 2-D theory. A learning law is proposed for a class of linear discrete systems. A learning law is studied for a class of linear discrete systems with delays.3. The initial state problems of ILC are studied. Two learning laws with initial state learning and random initial states are proposed, and their convergence and robustness are proved.4. Iterative learning control application is investigated by project examples. The applications of iterative learning control in robot systems and power filter system are studied.