Nonlinear Observers for Spacecraft Attitude Estimation in Case of Yaw Angle Measurement Absence

被引:5
作者
Aly, Mohamed M. [1 ]
Fatah, Hossam A. Abdel [2 ]
Bahgat, Ahmed [2 ]
机构
[1] Old Dominion Univ, Dept Aerosp Engn, Norfolk, VA 23529 USA
[2] Cairo Univ, Fac Engn, Elect Power & Machines Dept, Giza 12613, Egypt
关键词
EKF; nonlinear observability; sliding mode observer; spacecraft attitude; stability;
D O I
10.1007/s12555-010-0511-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper provides a brief presentation and a useful comparison between two nonlinear observers Extended Kalman Filter (EKF) and sliding mode observer (SMO). Both can be used for moderate-accuracy attitude determination systems for Low Earth Orbit (LEO) Earth-pointing spacecraft (s/c), which is typically achieved using Gyroscopes, Earth, and Sun sensors for attitude sensing. The use of these observers provides a substitute for the yaw data in case of the s/c eclipse periods or limited field of views. The nonlinear observability for this system is analytically investigated via the calculation of Lie derivatives to check the possibility of the system states estimation. The performances of both observers are presented, the SMO stability is proved and the SMO enhanced estimates are shown by simulation.
引用
收藏
页码:1018 / 1028
页数:11
相关论文
共 21 条
[1]  
[Anonymous], 1978, Spacecraft Attitude Determination and Control
[2]  
BAK T, 1999, THESIS AALBORG U DEN
[3]   Reduction of propagated satellite yaw errors using orbital rate coupling [J].
Bruno, D .
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2000, 23 (03) :554-556
[4]  
CHBOTOV AV, 1991, ORBIT FDN SERIES
[5]   SLIDING-MODE CONTROLLER-DESIGN FOR SPACECRAFT ATTITUDE TRACKING MANEUVERS [J].
CHEN, YP ;
LO, SC .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1993, 29 (04) :1328-1333
[6]  
Crassidis J. L., 2007, J GUIDANCE CONTROL D, V30
[7]   Unscented filtering for spacecraft attitude estimation [J].
Crassidis, JL ;
Markley, FL .
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2003, 26 (04) :536-542
[8]  
Grewal M.S., 2014, Kalman Filtering: Theory and Practice Using MATLAB
[9]  
GROCOTT S, 2000, P 14 AIAA USU C SMAL
[10]  
Haykin S, 2001, ADAPT LEARN SYST SIG, P1