Adaptive Iterative Learning Control for Spacecraft Close-Proximity Operations With Uncertainties

This article presents an adaptive control scheme incorporated with iterative learning framework for spacecraft close-proximity operations. A simple adaptive control algorithm is prewraped into spacecraft relative dynamics to formulate a strictly output passive input-output map. Iterative learning control is then used in conjunction to enhance tracking control performance based on previous control execution. The relative trajectory tracking errors between two spacecraft are monotonically decreased during consecutive operating cycles, and such convergence is guaranteed by passivity theory. The advantage of the proposed method lies in that the close-proximity control performance of the current iteration cycle is improved by learning the control experience accumulated in the previous iteration cycle. Numerical simulations are taken to show the effectiveness of the proposed adaptive iterative learning control scheme. Model uncertainties, as well as external perturbations are also considered in simulation to examine the robustness of the closed-loop system.