A Dynamic Recurrent Neural Network Fault Diagnosis and Isolation Architecture for Satellite’s Actuator/Thruster Failures

Abstract

In this paper, a fault diagnosis and isolation (FDI) strategy based on a Dynamically Driven Recurrent Neural Network (DDRNN) architecture is proposed for use in situations when there are thruster/actuator failures in the satellite’s attitude control system. The proposed architecture is motivated from the following facts: (1) the satellite’s attitude dynamics is highly complex and nonlinear, (2) the large volume of data that is generated in the attitude control system has to be monitored in real-time by the ground station operators which is a highly labor-intensive and time-consuming task, and (3) dynamically driven recurrent neural networks (DDRNN) have been shown to have the ability to learn, recognize and generate complex temporal patterns. To improve the FDI performance accuracy, the proposed architecture is designed to consist of two DDRNNs. The first DDRNN determines and diagnoses the presence of a faulty thruster. The second DDRNN then identifies which thruster is faulty, i.e. it isolates the location of the fault. Extensive simulation results are shown that demonstrate and verify that the proposed two DDRNNs scheme is more efficient and robust as compared to a scheme that is based on a single feed-forward back-propagation neural network or a single DDRNN scheme, especially in the presence of external disturbances and noise.