Detection of actuator faults using a dynamic neural network for the attitude control subsystem of a satellite

The main objective of this paper is to develop a neural network-based residual generator for fault detection (FD) in the attitude control subsystem (ACS) of a satellite. Towards this end, a dynamic multilayer perceptron (DMLP) network with dynamic neurons is considered. The neuron model consists of a second order linear IIR filter and a nonlinear activation function with adjustable parameters. Based on a given set of input-output data pairs collected from the attitude control subsystem, the network parameters are adjusted to minimize a performance index specified by the output estimation error. The proposed dynamic neural network structure is applied for detecting faults in a reaction wheel (RW) that is often used as an actuator in the ACS of a satellite. The performance and capabilities of the proposed dynamic neural network is investigated and compared to a model-based observer residual generator design that is to detect various fault scenarios.