Norm optimal Iterative Learning Control with auxiliary optimization - An inverse model approach

An Iterative Learning Control (ILC) algorithm is derived to address the problem in which tracking is only required at selected intermediate points within the time interval while an auxiliary function is simultaneously minimized. This is driven by the needs of robotic automation tasks where point-to-point motion control is combined with a need to reduce payload spillage, vibration tendencies and actuator wear. Experimental results confirm practical utility and theoretical performance.