Neural Network-Based Finite-Time Fault-Tolerant Control for Spacecraft without Unwinding | Zendy

Chao Tan | Zendy; Guodong Xu | Zendy; Limin Dong | Zendy; Han Zhao | Zendy; Jun Li | Zendy; Sai Zhang | Zendy

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Neural Network-Based Finite-Time Fault-Tolerant Control for Spacecraft without Unwinding

Author(s) -

Chao Tan,

Guodong Xu,

Limin Dong,

Han Zhao,

Jun Li,

Sai Zhang

Publication year - 2021

Publication title -

international journal of aerospace engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.361

H-Index - 22

eISSN - 1687-5974

pISSN - 1687-5966

DOI - 10.1155/2021/9269438

Subject(s) - control theory (sociology) , backstepping , artificial neural network , parametric statistics , fault tolerance , sylvester's law of inertia , actuator , computer science , convergence (economics) , control reconfiguration , spacecraft , lyapunov function , controller (irrigation) , fault (geology) , control engineering , inertia , reliability (semiconductor) , engineering , adaptive control , control (management) , artificial intelligence , mathematics , nonlinear system , symmetric matrix , distributed computing , aerospace engineering , economic growth , biology , embedded system , power (physics) , classical mechanics , quantum mechanics , agronomy , statistics , eigenvalues and eigenvectors , physics , seismology , economics , geology

In this paper, we focus on solving the problems of inertia-free attitude tracking control for spacecraft subject to external disturbance, unknown inertial parameters, and actuator faults. The robust control architecture is designed by using the rotation matrix and neural networks. In the presence of external disturbance and parametric uncertainties, a fault-tolerant control (FTC) scheme synthesized with the minimum-learning-parameter (MLP) algorithm is proposed to improve the reliability of the system when unknown actuator faults occur. These methods are developed based on backstepping to ensure that finite-time convergence is achievable for the entire closed-loop system states with low computational complexity. The validity and advantage of the designed controllers are highlighted by using Lyapunov-based analysis. Finally, the simulation results demonstrate the satisfactory performance of the developed controllers.

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