Statistical plant set estimation using Schroeder-phased multisinusoidal input design
Applied Mathematics and Computation
Automatica (Journal of IFAC)
Closed-loop identification revisited
Automatica (Journal of IFAC)
Bias of indirect non-parametric transfer function estimates for plants in closed loop
Automatica (Journal of IFAC)
Hi-index | 22.14 |
It is known that non-parametric transfer function estimates in closed-loop often have infinite variance. We characterise the probability density function of such estimates under the assumption that the corresponding closed-loop system estimate has complex normal distribution in the frequency domain. The probability density function can be described as a horseshoe encircling the inverse of the controller, with a global maximum on the line between the true value and the inverse of the controller. The expected value of the absolute value of such estimates is finite, and we propose it as a measure of variation. We also derive and discuss new expressions for the variance when an exclusion zone is introduced around the singularity.