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Dynamic neural network-based Pulsed Plasma Thruster (PPT) fault detection and isolation for the attitude control system of a satellite

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2 Author(s)
Valdes, A. ; Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, QC ; Khorasani, K.

The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) of a satellite. The goal is to determine the occurrence of a fault in any one of the multiple thrusters that are employed in the attitude control subsystem of a satellite, and further to localize which PPT is faulty. In order to accomplish these objectives, a multilayer perceptron network embedded with dynamic neurons is proposed. Based on a given set of input-output data collected from the electrical circuit of the PPTs, the dynamic network parameters are adjusted to minimize the output estimation error. A Confusion Matrix approach is used to measure the effectiveness of our proposed dynamic neural network-based fault detection and isolation (FDI) scheme under various fault scenarios.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008