Fault Diagnosis of an Actuator in the Attitude Control Subsystem of a Satellite using Neural Networks

Abstract

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.