Abstract: The objective of this paper is to develop 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 dynamic multilayer perceptron network with dynamic
neurons is used, those neurons correspond 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 proposed 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 proposed
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 algorithm to
know the real status of the momentum wheel. Finally, the application
of the methods in a satellite ground station is discussed.
Abstract: Self-sensing estimates the air gap within an electro
magnetic path by analyzing the bearing coil current and/or voltage
waveform. The self-sensing concept presented in this paper has been
developed within the research project “Active Magnetic Bearings
with Supreme Reliability" and is used for position sensor fault
detection.
Within this new concept gap calculation is carried out by an alldigital
analysis of the digitized coil current and voltage waveform.
For analysis those time periods within the PWM period are used,
which give the best results. Additionally, the concept allows the
digital compensation of nonlinearities, for example magnetic
saturation, without degrading signal quality. This increases the
accuracy and robustness of the air gap estimation and additionally
reduces phase delays.
Beneath an overview about the developed concept first
measurement results are presented which show the potential of this
all-digital self-sensing concept.