DocumentCode :
536358
Title :
Sliding mode control using RBF neural network for spacecraft attitude tracking
Author :
Chen, Shiming ; Dong, Yunfeng ; Su, Jianmin
Author_Institution :
Sch. of Astronaut., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
Volume :
2
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
211
Lastpage :
214
Abstract :
In order to avoid inherent chattering of sliding mode control, a radial basis function neural network based sliding mode control is presented. By using four reaction wheels and Modified Rodrigues Parameters for attitude tracking representation, the tracking dynamic has been considered, and inertia matrix uncertainty, actuators uncertainty and external disturbances has been considered in the model. Divide the controller into two parts, one is the traditional sliding mode control, and the other part is neural network to estimating the system´s uncertainties. The Lyapunov stability theory has been used to achieve a stable closed loop system. Simulation results illustrate the performance of the proposed algorithm. The controller successfully deals with unknown misalignments of the axis directions of the actuators, inertia matrix uncertainty and external disturbance torques.
Keywords :
Lyapunov matrix equations; actuators; attitude control; closed loop systems; nonlinear control systems; radial basis function networks; space vehicles; uncertain systems; variable structure systems; wheels; Lyapunov stability theory; RBF neural network; actuators uncertainty; external disturbances; inertia matrix uncertainty; inherent chattering; modified Rodrigues parameters; radial basis function neural network; sliding mode control; spacecraft attitude tracking; stable closed loop system; wheels;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6582-8
Type :
conf
DOI :
10.1109/ICICISYS.2010.5658767
Filename :
5658767
Link To Document :
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