Hierarchical Guidance for Spacecraft Proximity via Iterative State Transitions

• Published in: IEEE transactions on aerospace and electronic systems Vol. 61; no. 2; pp. 5166 - 5177
• Main Authors: Yuan, Shuai, Wang, Yiyu, Zhang, Zexu, Fabiani, Filippo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace and electronic systems; Artificial potential function; B-splines; constrained optimal control; Constraints; Control methods; Convex functions; convex optimization; Convexity; Energy consumption; Optimal control; Optimization; Orbits; Parameterization; Predictive control; Quasi Newton methods; Safety; Space missions; Space vehicles; Spacecraft guidance; Spacecraft maneuvers; spacecraft proximity; Splines (mathematics); Symmetric matrices; System dynamics; system flatness; Trajectories; Trajectory; Vectors
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2024.3520081

In this article, we propose a hierarchical guidance framework for spacecraft proximity tasks subject to motion and path constraints by integrating artificial potential functions and optimization methods. The overall guidance methodology consists of two main steps: 1) iterative generation of trajectory points and 2) state transition between every consecutive pair of those points. An artificial potential function incorporating the constraints is proposed in the form of a barrier function, based on which the trajectory points are then generated by iteratively approaching the target through a quasi-Newton method. The state transition guidance, instead, is formulated as a constrained optimal control problem aiming at minimizing the energy consumption while incorporating system dynamics and motion and path constraints. We show that the latter can be turned into a convex optimization problem using the system flatness and the B-spline parameterization, thus alleviating the required computational burden. The contribution of the proposed guidance and control method consists of two aspects: 1) providing a framework to fulfill performance optimization for the conventional artificial potential function methods and 2) reducing the computational burden compared to a standard model-predictive control method. Extensive numerical simulations confirm this fact, along with showing the effectiveness of our method to guarantee safe and fast spacecraft proximity maneuvers.