A neural network-based fault detection scheme for satellite attitude control systems

This paper presents an actuator fault detection and identification (FDI) scheme for satellite attitude control systems. A state-space approach is used and a nonlinear-in-parameters neural network (NLPNN) is employed to identify the general unknown fault. The recurrent network configuration is obtained by a combination of feedforward network architectures and dynamical elements in the form of stable filters. The neural network weights are updated based on a modified backpropagation scheme. The stability of the overall fault detection scheme is shown using Lyapunov's direct method. Simulation results are presented to show the performance of the proposed fault detection scheme

[1]  Bernd Freyermuth.  An approach to model based fault diagnosis of industrial robots , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[2]  R. Tinos,et al.  Fault detection and isolation in robotic systems via artificial neural networks , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[3]  P. S. Goel,et al.  A scheme for fault tolerance in earth sensors , 1989 .

[4]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[5]  Jean-Jacques E. Slotine,et al.  Hamiltonian adaptive control of spacecraft , 1990 .

[6]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[7]  Weifeng Tian,et al.  Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[8]  R. Tinos,et al.  Fault detection and isolation in cooperative manipulators via artificial neural networks , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[9]  Y. C. Chen,et al.  A neural network application to fault diagnosis for robotic manipulator , 1996, Proceeding of the 1996 IEEE International Conference on Control Applications IEEE International Conference on Control Applications held together with IEEE International Symposium on Intelligent Contro.

[10]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[11]  David S. Bayard,et al.  Adaptive Kalman filtering, failure detection and identification for spacecraft attitude estimation , 1995, Proceedings of International Conference on Control Applications.

[12]  Ciann-Dong Yang,et al.  Nonlinear H/sub /spl infin// robust control for satellite large angle attitude maneuvers , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[13]  Paul M. Frank,et al.  Observer-based supervision and fault detection in robots using nonlinear and fuzzy logic residual evaluation , 1996, IEEE Trans. Control. Syst. Technol..

[14]  Edoardo Mosca,et al.  Attitude control of asymmetric spacecrafts subject to actuator failures , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[15]  Marios M. Polycarpou,et al.  Neural network based fault detection in robotic manipulators , 1998, IEEE Trans. Robotics Autom..

[16]  Joseph R. Cavallaro,et al.  Expert system framework for fault detection and fault tolerance in robotics , 1994 .

[17]  Qing Li,et al.  Application of neural networks for sensor fusion in a remote sensing satellite , 2002, Proceedings of the IEEE Internatinal Symposium on Intelligent Control.

[18]  Thomas Bak,et al.  Autonomous Attitude Determination and Control System for the Ørsted Satellite 1996 , 1996 .