The objective of this paper is to design a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system (ACS). First, a design of dynamic neural network with dynamic neurons is done, those neurons corresponds a second order linear Infinite Impulse Response (IIR) filter and a nonlinear activation function with adjustable parameters. Second, the parameters from the network are adjusted to minimize a performance index specified by the output estimated error, with the given input-output data collected from the specific ACS. Then, the designed dynamic neural network is trained and applied for detecting the faults injected to the wheel, which is the main actuator in the normal mode for the communication satellite. Then the performance and capabilities of the designed network were tested and compared with a conventional model-based observer residual, showing the differences between these two methods, and indicating the benefit of the proposed design to know the real status of the momentum wheel. Finally, the application of the methods in a satellite ground station is discussed.
Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach
