Exploiting the use of noncausal finite time interval data in iterative learning control law design

Iterative learning control has been developed for processes or systems that complete the same finite duration task over and over again. After each execution is complete, the system resets to the initial location, or a stoppage time occurs, and then the next execution can begin. In the literature each execution is commonly known as a trial and the duration is termed the trial length. Once a trial is complete, all information generated over the trial length is available for use in computing the control input to be applied on the next trial. This includes information that would be non-causal in the standard sense and the availability of such information for control purposes is the major novel feature. The repetitive process setting for analysis and design allows for a general treatment of the use of non-causal and causal previous trial information in design and this paper gives new design oriented results on how this design freedom can be best exploited.