A high-order fully actuated predictive control approach for spacecraft flying-around

Abstract

Under the framework of high-order fully actuated (HOFA) system theory, this study investigates a spacecraft flying-around problem in a sight coordinate system. In this sight coordinate system, a nonlinear HOFA system model is applied to establish the relative dynamics of spacecraft flying-around, such that the presented flying-around mission can be transformed into a tracking control problem of nonlinear HOFA systems. A HOFA predictive control approach is correspondingly proposed to deal with this problem. Concretely, the nonlinearities can be eliminated to construct a linear HOFA system by utilising full actuation characteristic, and then a linear incremental HOFA (LIHOFA) prediction model is constructed by applying a Diophantine equation to replace a reduced-order prediction model. Based on this LIHOFA prediction model, multi-step ahead predictions are developed to optimise the tracking control performance, so that the realisation of the presented flying-around mission can be guaranteed. A sufficient and necessary condition is derived in the in-depth discussion to maintain the stability and tracking control performance of closed-loop systems. Furthermore, numerical simulation and comparison about spacecraft flying-around in circular and elliptical orbits are provided to verify the feasibility of the HOFA predictive control approach. To highlight its practicability, an experimental verification via air-bearing spacecraft (ABS) simulator is shown for the implementation of flying-around mission.

Keywords:

• Spacecraft flying-around
• nonlinear HOFA systems
• HOFA predictive control approach
• stability and tracking control performance
• experimental verification via ABS simulator

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant numbers 62173255 and 62188101], and in part by the Shenzhen Key Laboratory of Control Theory and Intelligent Systems [grant number ZDSYS20220330161800001].