scholarly journals Feedback Control for Satellite Orbit Transfer by a Tethered Satellite System

2009 ◽  
Vol 57 (666) ◽  
pp. 301-307
Author(s):  
Shinji HOKAMOTO ◽  
Shohei KOGA
Keyword(s):  
Feedback Control ◽  
Satellite Orbit ◽  
Satellite System ◽  
Orbit Transfer ◽  
Tethered Satellite ◽  
Tethered Satellite System

Current Collection Model Characterizing Shuttle Charging During the Tethered Satellite System Missions

2000 ◽  
Vol 37 (2) ◽  
pp. 212-217 ◽  
Author(s):  
Victor M. Aguero ◽  
Brian E. Gilchrist ◽  
Scott D. Williams ◽  
William J. Burke ◽  
Linda Krause ◽  
...  
Keyword(s):  
Satellite System ◽  
Tethered Satellite ◽  
Tethered Satellite System ◽  
Current Collection

Dynamic analysis of a tethered satellite system with a moving mass

2013 ◽  
Vol 75 (1-2) ◽  
pp. 267-281 ◽  
Author(s):  
Wonyoung Jung ◽  
Andre P. Mazzoleni ◽  
Jintai Chung
Keyword(s):  
Dynamic Analysis ◽  
Satellite System ◽  
Moving Mass ◽  
Tethered Satellite ◽  
Tethered Satellite System

Neural network-based adaptive terminal sliding mode control for the deployment process of the dual-body tethered satellite system

2019 ◽  
Vol 234 (6) ◽  
pp. 1157-1171 ◽  
Author(s):  
Chenguang Liu ◽  
Wei Wang ◽  
Yong Guo ◽  
Shumin Chen ◽  
Aijun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
External Disturbance ◽  
Satellite System ◽  
Terminal Sliding Mode ◽  
Tethered Satellite ◽  
Tethered Satellite System ◽  
Radial Basis

The dual-body tethered satellite system, which consists of two spacecraft connected by a single tether, is one of the most promising configurations in numerous space missions. To ensure the stability of deployment, the radial basis function neural network-based adaptive terminal sliding mode controller is proposed for the dual-body tethered satellite system with the model uncertainty and external disturbance. The terminal sliding mode controller serves as the main control framework for its properties of the strong robustness and finite-time convergence. The radial basis function neural network is adopted to approximate the model uncertainty, in which the weight vector of the radial basis function neural networks and the unknown upper bound of the external disturbance are estimated by using two adaptive laws. Finally, the Lyapunov theory and numerical simulations are used to prove the validity of the proposed controller.

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