Nonlinear resonant analysis of space tethered satellite system in elliptical orbits

2021 ◽  
Vol 182 ◽  
pp. 264-273
Author(s):  
Zhaojun Pang ◽  
Hao Wen ◽  
Xiaoting Rui ◽  
Zhonghua Du
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhaojun Pang ◽  
Zhonghua Du ◽  
Chun Cheng ◽  
Qingtao Wang

This paper studies resonance motions of a tethered satellite system (TSS) in elliptical orbits. A perturbation analysis is carried out to obtain all possible resonance types and corresponding parameter relations, including internal resonances and parametrically excited resonances. Besides, a resonance parametric domain is given to provide a reference for the parameter design of the system. The bifurcation behaviors of the system under resonances are studied numerically. The results show that resonant cases more easily enter chaotic motion than nonresonant cases. The extended time-delay autosynchronization (ETDAS) method is applied to stabilize the chaotic motion to a periodic one. Stability analysis shows that the stable domains become smaller in resonance cases than in the nonresonance case. Finally, it is shown that the large amplitudes of periodic solutions under resonances are the main reason why the system is difficult to control.


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

2013 ◽  
Vol 75 (1-2) ◽  
pp. 267-281 ◽  
Author(s):  
Wonyoung Jung ◽  
Andre P. Mazzoleni ◽  
Jintai Chung

Author(s):  
Chenguang Liu ◽  
Wei Wang ◽  
Yong Guo ◽  
Shumin Chen ◽  
Aijun Li ◽  
...  

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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