The role of 2D linear systems theory in the design and experimental verification of iterative learning control algorithms

Iterative learning control algorithms are applicable to the many industrial processes that complete the same finite duration task over and over again. One example is a gantry robot executing a pick and place operation in synchronization with a moving conveyor. The novel feature of these algorithms is the use of information from previous executions to update the control input for application on the next one. Iterative learning control has a 2D systems structure and this paper will give an overview of recent progress on the design of iterative learning control laws in this setting. A particular focus will be on the relative merits of this approach over alternatives and also experimental verification. Open research questions will also be discussed.