STOCHASTIC MODEL PREDICTIVE CONTROL FOR SPACECRAFT RENDEZVOUS AND DOCKING VIA A DISTRIBUTIONALLY ROBUST OPTIMIZATION APPROACH

• Published in: The ANZIAM journal Vol. 63; no. 1; pp. 39 - 57
• Main Authors: LI, ZUOXUN, ZHANG, KAI
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.01.2021
• Subjects: Aerospace engineering; Algorithms; Attitude control; Computational geometry; Convexity; Electrical engineering; Obstacle avoidance; Optimization; Predictive control; Probability density functions; Robust control; Space rendezvous; Space stations; Spacecraft; Spacecraft docking; Stochastic models; rendezvous and docking; 93B45; stochastic model predictive control; Monte Carlo; 49N10
• ISSN: 1446-1811; 1446-8735
• DOI: 10.1017/S1446181121000031

A stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.