Segment-Based Two-Loop Adaptive Iterative Learning Control for Spacecraft Position and Attitude Tracking

TL;DR

This paper introduces a novel segment-based dual-loop adaptive iterative learning control framework for precise spacecraft position and attitude tracking, effectively handling uncertainties and coupling effects in repeated proximity operations.

Contribution

It proposes a dual-number-based, segment-based adaptive ILC approach that unifies position and attitude error handling and guarantees bounded control inputs without prior uncertainty knowledge.

Findings

Enhanced tracking accuracy under uncertainties.

Bounded control inputs achieved without prior bounds.

Effective handling of rotational-translational coupling.

Abstract

Proximity operations of rigid bodies, such as spacecraft rendezvous and docking, require precise tracking of both position and attitude over finite time intervals. These operations are often repeated under uncertain conditions, with unknown but repeatable parameters and disturbances. Adaptive iterative learning control (ILC) is well suited to such tasks, as it can track desired trajectories while learning unknown, iteration-invariant signals or parameters. However, conventional adaptive ILC faces two challenges: (i) the coupling between rotational and translational dynamics complicates the design of the two coordinated learning loops for position and attitude, and (ii) standard adaptive ILC designs cannot guarantee bounded control inputs. To address these issues, we propose a dual-number-based, segment-based two-loop adaptive ILC framework for simultaneous high-precision position and attitude tracking. The framework employs two learning loops that interact through a dual-number representation of tracking errors, combining position and attitude errors into a single mathematical object for unified control design. A segment-based dynamic projection mechanism ensures that both parameter estimates and control inputs remain bounded without prior knowledge of uncertainties. Mathematical analysis and numerical simulations demonstrate that the proposed framework significantly enhances tracking performance under unknown but repeatable uncertainties and strong rotational-translational coupling.