中文

Journal of Applied Sciences ›› 2010, Vol. 28 ›› Issue (1): 72-76.

• Control and System • Previous Articles     Next Articles

Self-Organizing Fuzzy Neural Network-Based Actuator Fault Estimation for Satellite Attitude Systems

CHENG Yue-hua1;2, JIANG Bin1, YANG Ming-kai1, GAO Zhi-feng1   

  1. 1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
    Nanjing 210016, China
    2. Academy of Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    Abstract:
  • Received:2009-06-01 Revised:2009-10-22 Online:2010-01-20 Published:2010-01-20

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Abstract:

Weights and nodes of a self-organizing fuzzy neural network (SOFNN) can be updated online for network structure optimization. This paper studies a robust fault diagnostic approach based on two SOFNNs for a class of satellite attitude dynamics. The designed SOFNN1 is used to estimate uncertainties and external perturbations of fault-free satellite attitude dynamics, whose output is chosen as a referenced threshold of fault
detection. Based on SOFNN1, SOFNN2 is constructed to estimate actuator faults occurring in the satellite attitude dynamics. Simulation results demonstrate that SOFNN has good dynamics performance in estimating actuator faults for the considered dynamics with external noise and system parameter uncertainties. Compared with fixed-structured FNN, the proposed SOFNN has advantages in estimation speed.

Key words: self-organizing fuzzy neural network, fault estimation, satellite, actuator

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