Detection of Actuator Faults for an Attitude Control System using Neural Network
The objective of this paper is to develop a neural
network-based residual generator to detect the fault in the actuators
for a specific communication satellite in its attitude control system
(ACS). First, a dynamic multilayer perceptron network with dynamic
neurons is used, those neurons correspond a second order linear
Infinite Impulse Response (IIR) filter and a nonlinear activation
function with adjustable parameters. Second, the parameters from the
network are adjusted to minimize a performance index specified by
the output estimated error, with the given input-output data collected
from the specific ACS. Then, the proposed dynamic neural network
is trained and applied for detecting the faults injected to the wheel,
which is the main actuator in the normal mode for the communication
satellite. Then the performance and capabilities of the proposed
network were tested and compared with a conventional model-based
observer residual, showing the differences between these two
methods, and indicating the benefit of the proposed algorithm to
know the real status of the momentum wheel. Finally, the application
of the methods in a satellite ground station is discussed.
[1] R. Isermann "Fault-Diagnosis System," Springer-Verlag Berlin
Heidelberg. 2006.
[2] R. Patton, F Uppal and C. Lopez-Toribio, "Soft computing approaches
to fault diagnosis for dynamic systems: a survey," 4th. IFAC
Symposium on fault Detection Supervision and Safety for technical
processes, Budapest, Vol. 1, pp. 298-311, June 2000.
[3] S. Montenegro and K. Amezquita, "Accomplishing Station Keeping
Mode for AOCS designed for T-SAT," Advances in Neural Network
Research, The sixth ISNN 2009, Wuhan, China, pp. 507-516. Springer-
Verlag, 2009.
[4] A. Alessandri, "Fault diagnosis for nonlinear systems using a bank of
neural estimators," Computers in Industry, Vol. 52, No. 3, pp. 271 -
289, Dec. 2003.
[5] K. Patan, T. Parisini, "Identification of neural dynamic models for fault
detection and isolation: the case of a real sugar evaporation process"
Journal of Process Control, Vol. 15, No. 1, pp. 67 - 79, 2004.
[6] T. Sorsa, H. N. Koivo, and H. Koivisto, "Neural networks in process
fault diagnostics," IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 21, No.4, 815 - 825, 1991.
[7] S. Simani, C. Fantuzzi and R. J. Patton, "Model-Based Fault Diagnosis
in Dynamic Systems Using Identification Techniques," Springer 2003
[8] J. Wertz. "Spacecraft Attitude Determination and Control." Kluwer
Academic Publishers, 1978.
[9] M. J. Sidi. "Spacecraft Dynamics and Control." Cambridge University
Press, Cambridge New York, 1997
[10] B Bialke, "High fidelity mathematical modelling of reaction wheel
performance," 21st Annual American Astronautical Society Guidance
and Control Conference, pp. 483-486, Feb. 1998.
[11] W. Qing and S. Mehrdad "Model-based Robust Fault Diagnosis for
Satellite Control Systems Using Learning and Sliding Mode
Approaches," Journal of Computers, Vol. 4, No. 10, Simon Fraser
University, Vancouver, BC, Canada. 2009.
[12] M. Ayoubi, "Fault diagnosis with dynamic neural structure and
application to a turbo-charger," Proc. Int. Symp. Fault Detection
Supervision and Safety for Technical Processes, AFEPROCESS'94,
Espoo, Finland, Vol. 2, pp. 618 - 623, 1994.
[13] J. Korbicz, K. Patan, A. Obuchowicz, "Dynamic neural networks for
process modeling in fault detection and isolation systems,"
International Journal of Applied Mathematics and Computer Science,
Vol. 9, No. 3, pp. 519- 546, 1999.
[14] I. Al-Zyoud and K. Khorasani, "Neural network-based Actuactor Fault
Diagnosis for Attitude Control Subsystem of an Unmanned Space
Vehicle," International Joint Conference on Neural Networks,
Vancouver, Canada. pp 3686-3693. July, 2006.
[15] Z. Li, L. Ma and K. Khorasani, "Dynamic Neural Network-Based Fault
diagnosis for attitude control subsystem of a satellite," PRICAI, LNAI
4099, pp 308-318, Springer-Verlag Berlin Heidelberg 2006.
[16] Z. Li, L. Ma and K. Khorasani, "Fault Detection in Reaction Wheel of a
satellite using Observer-Based Dynamic Neural Networks" ISNN 2005,
LNCS 3498, pp. 584-590, Springer- Verlag Berling Heidelberg, 2005.
[17] T. Marcu, L Mirea and P. Frank, "Neural observer schemes for robust
detection and isolation of process faults," UKACC International
Conference on Control-98, September 1998. Conference Publication
No. 455. IEEE, 1998.
[18] P. C. Hughes, "Spacecraft Attitude Dynamics." John Wiley & Sons
(1986).
[19] V. Chobotov, "Spacecraft Attitude Dynamics and Control." Krieger
Publishing Company. Malabar, Florida. USA. 1991.
[1] R. Isermann "Fault-Diagnosis System," Springer-Verlag Berlin
Heidelberg. 2006.
[2] R. Patton, F Uppal and C. Lopez-Toribio, "Soft computing approaches
to fault diagnosis for dynamic systems: a survey," 4th. IFAC
Symposium on fault Detection Supervision and Safety for technical
processes, Budapest, Vol. 1, pp. 298-311, June 2000.
[3] S. Montenegro and K. Amezquita, "Accomplishing Station Keeping
Mode for AOCS designed for T-SAT," Advances in Neural Network
Research, The sixth ISNN 2009, Wuhan, China, pp. 507-516. Springer-
Verlag, 2009.
[4] A. Alessandri, "Fault diagnosis for nonlinear systems using a bank of
neural estimators," Computers in Industry, Vol. 52, No. 3, pp. 271 -
289, Dec. 2003.
[5] K. Patan, T. Parisini, "Identification of neural dynamic models for fault
detection and isolation: the case of a real sugar evaporation process"
Journal of Process Control, Vol. 15, No. 1, pp. 67 - 79, 2004.
[6] T. Sorsa, H. N. Koivo, and H. Koivisto, "Neural networks in process
fault diagnostics," IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 21, No.4, 815 - 825, 1991.
[7] S. Simani, C. Fantuzzi and R. J. Patton, "Model-Based Fault Diagnosis
in Dynamic Systems Using Identification Techniques," Springer 2003
[8] J. Wertz. "Spacecraft Attitude Determination and Control." Kluwer
Academic Publishers, 1978.
[9] M. J. Sidi. "Spacecraft Dynamics and Control." Cambridge University
Press, Cambridge New York, 1997
[10] B Bialke, "High fidelity mathematical modelling of reaction wheel
performance," 21st Annual American Astronautical Society Guidance
and Control Conference, pp. 483-486, Feb. 1998.
[11] W. Qing and S. Mehrdad "Model-based Robust Fault Diagnosis for
Satellite Control Systems Using Learning and Sliding Mode
Approaches," Journal of Computers, Vol. 4, No. 10, Simon Fraser
University, Vancouver, BC, Canada. 2009.
[12] M. Ayoubi, "Fault diagnosis with dynamic neural structure and
application to a turbo-charger," Proc. Int. Symp. Fault Detection
Supervision and Safety for Technical Processes, AFEPROCESS'94,
Espoo, Finland, Vol. 2, pp. 618 - 623, 1994.
[13] J. Korbicz, K. Patan, A. Obuchowicz, "Dynamic neural networks for
process modeling in fault detection and isolation systems,"
International Journal of Applied Mathematics and Computer Science,
Vol. 9, No. 3, pp. 519- 546, 1999.
[14] I. Al-Zyoud and K. Khorasani, "Neural network-based Actuactor Fault
Diagnosis for Attitude Control Subsystem of an Unmanned Space
Vehicle," International Joint Conference on Neural Networks,
Vancouver, Canada. pp 3686-3693. July, 2006.
[15] Z. Li, L. Ma and K. Khorasani, "Dynamic Neural Network-Based Fault
diagnosis for attitude control subsystem of a satellite," PRICAI, LNAI
4099, pp 308-318, Springer-Verlag Berlin Heidelberg 2006.
[16] Z. Li, L. Ma and K. Khorasani, "Fault Detection in Reaction Wheel of a
satellite using Observer-Based Dynamic Neural Networks" ISNN 2005,
LNCS 3498, pp. 584-590, Springer- Verlag Berling Heidelberg, 2005.
[17] T. Marcu, L Mirea and P. Frank, "Neural observer schemes for robust
detection and isolation of process faults," UKACC International
Conference on Control-98, September 1998. Conference Publication
No. 455. IEEE, 1998.
[18] P. C. Hughes, "Spacecraft Attitude Dynamics." John Wiley & Sons
(1986).
[19] V. Chobotov, "Spacecraft Attitude Dynamics and Control." Krieger
Publishing Company. Malabar, Florida. USA. 1991.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:60016", author = "S. Montenegro and W. Hu", title = "Detection of Actuator Faults for an Attitude Control System using Neural Network", abstract = "The objective of this paper is to develop a neural
network-based residual generator to detect the fault in the actuators
for a specific communication satellite in its attitude control system
(ACS). First, a dynamic multilayer perceptron network with dynamic
neurons is used, those neurons correspond a second order linear
Infinite Impulse Response (IIR) filter and a nonlinear activation
function with adjustable parameters. Second, the parameters from the
network are adjusted to minimize a performance index specified by
the output estimated error, with the given input-output data collected
from the specific ACS. Then, the proposed dynamic neural network
is trained and applied for detecting the faults injected to the wheel,
which is the main actuator in the normal mode for the communication
satellite. Then the performance and capabilities of the proposed
network were tested and compared with a conventional model-based
observer residual, showing the differences between these two
methods, and indicating the benefit of the proposed algorithm to
know the real status of the momentum wheel. Finally, the application
of the methods in a satellite ground station is discussed.", keywords = "Satellite, Attitude Control, Momentum Wheel,
Neural Network, Fault Detection.", volume = "4", number = "11", pages = "1267-7", }
