Scaling-based Least Squares Methods with Implemented Kalman filter Approach for Nano-Parameters Identification
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In: International Journal of Modelling, Identification and Control, Vol. 25, No. 2, 2016, p. 85-92.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Scaling-based Least Squares Methods with Implemented Kalman filter Approach for Nano-Parameters Identification
AU - Schimmack, Manuel
AU - Mercorelli, Paolo
PY - 2016
Y1 - 2016
N2 - A single-input and single-output (SISO) controlled autoregressive moving average system with scaled input-output data is considered here. Recursive least squares (RLSs) methods were used to estimate the nanosized parameters of a SISO linear model using input-output scaling factors. Thus, a general identification technique, through scaling data, was produced. Different variations of the RLS method were tested and compared. The first RLS method used a forgetting factor and the second method integrated a Kalman filter covariance. Using the described method, in order to estimate the resistance, time constant and inductance, the latter two lying within the nano range, the input signal must have both a high frequency and a high sampling rate, in relation to the time constant. The method developed here can be used to identify the nano parameters characterising the linear model, while allowing for a broader sampling rate and an input signal with lower frequency. Simulation results indicate that the proposed algorithm is both effective and robust at estimating the nano range parameters. The most powerful contribution contained here is the provision of a scaled identification bandwidth and sampling rate for the detecting signal in the identification process.
AB - A single-input and single-output (SISO) controlled autoregressive moving average system with scaled input-output data is considered here. Recursive least squares (RLSs) methods were used to estimate the nanosized parameters of a SISO linear model using input-output scaling factors. Thus, a general identification technique, through scaling data, was produced. Different variations of the RLS method were tested and compared. The first RLS method used a forgetting factor and the second method integrated a Kalman filter covariance. Using the described method, in order to estimate the resistance, time constant and inductance, the latter two lying within the nano range, the input signal must have both a high frequency and a high sampling rate, in relation to the time constant. The method developed here can be used to identify the nano parameters characterising the linear model, while allowing for a broader sampling rate and an input signal with lower frequency. Simulation results indicate that the proposed algorithm is both effective and robust at estimating the nano range parameters. The most powerful contribution contained here is the provision of a scaled identification bandwidth and sampling rate for the detecting signal in the identification process.
KW - Engineering
KW - Kalman filter approach, signal sampling
KW - Least squares methods
KW - Parameters identification
UR - http://www.scopus.com/inward/record.url?scp=84962581652&partnerID=8YFLogxK
U2 - 10.1504/IJMIC.2016.075269
DO - 10.1504/IJMIC.2016.075269
M3 - Journal articles
VL - 25
SP - 85
EP - 92
JO - International Journal of Modelling, Identification and Control
JF - International Journal of Modelling, Identification and Control
SN - 1746-6172
IS - 2
ER -