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Fault Diagnosis of an Actuator in the Attitude Control Subsystem of a Satellite using Neural Networks
By: Li, Z.Q.; Khorasani, K.; Ma, L.;
2007 / IEEE / 978-1-4244-1379-9
This item was taken from the IEEE Conference ' Fault Diagnosis of an Actuator in the Attitude Control Subsystem of a Satellite using Neural Networks ' The goal of this paper is to develop a neural network-based scheme for fault detection and isolation in reaction wheels (actuators) of a satellite. To achieve this objective, three neural networks are developed for modeling the dynamics of a reaction wheel on all the three axes separately. A recurrent neural network with backpropagation training algorithm is considered for representing the highly nonlinear dynamics of the actuator. The capabilities and potential of the proposed neural network-based fault detection and isolation (FDI) methodology is investigated and a comparative study is conducted with the performance of a generalized Luenberger observer-based scheme. Simulation results demonstrate clearly the advantages of our proposed neural network scheme studied in this paper.
Control Engineering Computing
Nonlinear Dynamical Systems
Recurrent Neural Nets
Luenberger Observer-based Scheme
Satellite Attitude Control Subsystem
Recurrent Neural Network
Reaction Wheel Nonlinear Dynamics Modeling
Backpropagation Training Algorithm
Fault Detection And Isolation
Actuator Fault Diagnosis