Authors :
Naleli Jubert Matjelo
Volume/Issue :
Volume 6 - 2021, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3kMnxWf
Abstract :
This paper gives an account of how power-law
noise power spectral density can be deconverted to white
noise power spectral density. The analysis is carried out
both in the frequency domain using transfer function
models as well as in the time domain using state-space
models whereby a linear time-invariant model being used
to generate approximate power-law noise from white noise
after which this model is inverted directly and indirectly.
Both direct (open-loop) model inversion and indirect
(closed-loop) model inversion are simulated and discussed.
It is through these simulations that the indirect model
inversion performance is shown to increases with
increasing feedback control gain
Keywords :
State-Space Model, Power-Law Noise, Model Inversion, Barnes-Jarvis Model, Feedback Control.
This paper gives an account of how power-law
noise power spectral density can be deconverted to white
noise power spectral density. The analysis is carried out
both in the frequency domain using transfer function
models as well as in the time domain using state-space
models whereby a linear time-invariant model being used
to generate approximate power-law noise from white noise
after which this model is inverted directly and indirectly.
Both direct (open-loop) model inversion and indirect
(closed-loop) model inversion are simulated and discussed.
It is through these simulations that the indirect model
inversion performance is shown to increases with
increasing feedback control gain
Keywords :
State-Space Model, Power-Law Noise, Model Inversion, Barnes-Jarvis Model, Feedback Control.
