Title: Scaling-based least squares methods with implemented Kalman filter approach for nano-parameters identification
Authors: Manuel Schimmack; Paolo Mercorelli
Addresses: Institute of Product and Process Innovation, Leuphana University of Lueneburg, Volgershall 1, D-21339 Lueneburg, Germany ' Institute of Product and Process Innovation, Leuphana University of Lueneburg, Volgershall 1, D-21339 Lueneburg, Germany
Abstract: 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.
Keywords: recursive least squares; RLS; parameter identification; Kalman filter; signal sampling; nano parameters; SISO ARMA; single-input and single-output; scaling data; nanotechnology; resistance; time constant; inductance; linear modelling; simulation.
International Journal of Modelling, Identification and Control, 2016 Vol.25 No.2, pp.85 - 92
Available online: 09 Mar 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article