Parameter Identification of Linear Time-Invariant Systems with Large Measurement Noises

in International Symposium (oral presentation paper), 國際研討會(全文口頭發表)
標題Parameter Identification of Linear Time-Invariant Systems with Large Measurement Noises
AuthorsHsun Heng Tsai, 蔡循恒, Chyun-Chau Fuh 傅群超, & H. - C. L.
出版日期Jun 12 2016 12:0

The parameter identification theories and algorithms have been developed well. For example, using the least squares and time-domain data to estimate the parameters of ARX (Autoregressive model with external input) or ARMAX (Autoregressive Moving-Average model with external input) models have become standard methods for estimating the parameters of linear time-invariant (LTI) systems. However, if we use the time-domain method to identify the parameters of a LTI system which input/output signals are disturbed by large noises, the results may cause serious error, even the estimated parameters become useless. In term of designing controllers, the engineers can choose or design more appropriate controllers, if they can know clearer or more accurate characteristics of the plants in advance. In this paper, we apply the Nelder-Mead simplex method to estimate the parameters of systems with large measurement noises based on frequency-domain. The simulation results show that using the simplex method based on frequency-domain, we can obtain more accurate models even the estimated systems including large measurement noises.

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