Robust neural-network-based quasi-sliding-mode control for spacecraft-attitude maneuvering with prescribed performance

被引：36

作者：

Fu, Jingbo ^{[1
]}

Liu, Ming ^{[1
]}

Cao, Xibin ^{[1
]}

Li, Ang ^{[1
,2
]}

机构：

[1] Harbin Inst Technol, Res Ctr Satellite Technol, Harbin 150001, Peoples R China

[2] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England

来源：

AEROSPACE SCIENCE AND TECHNOLOGY | 2021年 / 112卷

基金：

中国国家自然科学基金;

关键词：

Neural network; Prescribed performance control; Quasi sliding mode control; Spacecraft attitude maneuver;

D O I：

10.1016/j.ast.2021.106667

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

This paper investigates the robust tracking control problem for spacecraft attitude maneuvering with prescribed performance. Firstly, a control scheme is studied based on a quasi-sliding-mode approach which can simultaneously restrict the convergence speed and the steady-state error of both Euler angle and relative angular velocity. Then, to reduce actuators' energy consumption, a three-layer backpropagation neural network is combined with the above control law to auto-tune the control gains. Finally, the performance and effectiveness of the control approach are demonstrated by numerical simulation. (c) 2021 Elsevier Masson SAS. All rights reserved.

引用

页数：9

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