An On-line Robust Parameter Identification Algorithm
Date Issued
2010
Date
2010
Author(s)
Lin, Yi-Je
Abstract
This thesis proposes an on-line robust identification algorithm to estimate unknown parameters in a linear regression form that is contaminated by a deterministic disturbance signal. In this thesis, not only the parameters will be obtained by this algorithm, but also the disturbance will be estimated by an on-line polynomial fitting model without any disturbance information. In this algorithm, we use a polynomial fitting model with unknown coefficients to represent the disturbance in the system. Both unknown parameters of system and the time-varying disturbance will be estimated accurately by a Kalman filter observer. However, the results of the estimation will be affected by the choice of observer parameters. Under certain circumstances, the singular values of the solution in the Riccati equation will approach infinity. To prevent the singular values from increasing unlimitedly, a threshold for reset is set in order to ensure theis algorithm is capable of successfully estimating the real parameters.
Subjects
robust identification
parameter estimation
on-line system identification
Riccati Equation
threshold
Type
thesis
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