Analysis and experimental verification of a current-cycle iterative learning control for robotic manipulators with output constraints

The satisfaction of hard output constraints is one of the standard requirements in engineering applications. In particular, when robotic manipulators are working with humans, it is critical to satisfy the safety constraints (hard constraints). This paper focuses on designing iterative learning control algorithms for robotic manipulators with the consideration of hard output constraints. Practical issues such as sensitivity to measurement noises and soft input constraints are also considered in the design process. The convergence of tracking error is demonstrated using a suitable composite energy function based analysis. In addition, the experimental results are also presented to illustrate the effectiveness of the proposed controller.