NDLI: Neural Network-based Actuator Fault Diagnosis for Attitude Control Subsystem of an Unmanned Space Vehicle

Content Provider	IEEE Xplore Digital Library
Author	Al-Dein Al-Zyoud, I. Khorasani, K.
Copyright Year	2006
Description	Author affiliation: Concordia Univ., Montreal (Al-Dein Al-Zyoud, I.; Khorasani, K.)
Abstract	The main objective of this paper is to develop a neural network-based fault detection and isolation scheme (FDI) for the attitude control subsystem (ACS) of a satellite. Towards this end, two neural network architectures are considered. First, a dynamic neural network residual generator is constructed based on the dynamic multilayer perceptron (DMLP) network to perform the detection task. A generalized embedded structure for the dynamic neuron model is considered in the DMLP network. Second, a static neural classifier is developed based on learning vector quantization (LVQ) network to be utilized for the isolation task. Based on a given set of input-output data collected from a 3-axis ACS of a satellite, the network parameters are adjusted to minimize a performance index specified by the output estimation error. The proposed neural FDI structure is applied to detect and isolate various faults in a high-fidelity nonlinear model of a satellite reaction wheel (RW), which is often used as an actuator in the ACS. The performance and capabilities of the proposed techniques are investigated and compared to a model-based observer residual generator that is used to detect various fault scenarios.
Starting Page	3686
Ending Page	3693
File Size	336536
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247383
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Actuators Fault diagnosis Space vehicles Fault detection Satellites Vehicle dynamics Multi-layer neural network Multilayer perceptrons Neurons
Content Type	Text
Resource Type	Article

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