Dynamic Iterative Learning Control for Relative Trajectory Tracking with Nonrepetitive Leader Path

This paper proposes a collaborative iterative learning control scheme with optimal leader path planning strategy. Due to the demonds on repetitive tasks and static references under traditional ILC framework, it cannot fit the complex circumstances with multiple collaborative systems or satisfy certain control request on specific application. This paper introduces a dynamic norm-optimal iterative learning control (NOILC) framework based on an improved coordinate descent algorithm. Under the aforementioned framework, an optimal leader path can be found to fit certain performance requirements, i.e. energy reduction. Finally, the simulation results on a combined complex system verify the effectiveness of the proposed algorithm.