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

Part of: Miscellaneous topics in calculus of variations and optimal control

Published online by Cambridge University Press:  19 April 2021

ZUOXUN LI Open the ORCID record for ZUOXUN LI [Opens in a new window]  and
KAI ZHANG Open the ORCID record for KAI ZHANG [Opens in a new window]
ZUOXUN LI
Affiliation:
The College of Electrical Engineering, Sichuan University, 610065Chengdu, China; [email protected]
KAI ZHANG*
Affiliation:
The College of Electrical Engineering, Southwest Jiaotong University, 610031Chengdu, China
*
[email protected]
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Abstract

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.

Keywords

stochastic model predictive controlrendezvous and dockingMonte Carlo

MSC classification

Secondary: 49N10: Linear-quadratic problems
Type
Research Article
Information
The ANZIAM Journal , Volume 63 , Issue 1 , January 2021 , pp. 39 - 57
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Copyright
© Australian Mathematical Society 2021

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