Least square and Instrumental Variable system identification of ac servo position control system with fractional Gaussian noise

In this paper, the classical Least Square Estimator (LSE) and its improved version the Instrumental Variable (IV) estimator have been used for the identification of an ac servo motor position control system. The data for system identification has been collected from a practical test set-up for fixed command on the final angular position of the servo motor with varying level of velocity and acceleration. The measured data is corrupted then with externally induced random noise having a Gaussian distribution, commonly known as white Gaussian noise (wGn). Performance of the LSE and IV estimators are also compared for fractional Gaussian noise (fGn) which have heavy tails in its statistical distribution and are capable of modeling real world signals having spiky nature.

[1]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[2]  Shantanu Das,et al.  Denoising SPND signal by discrete wavelet analysis for efficient power feedback in regulating system of PHWRs under noisy environment , 2011, 2011 2nd National Conference on Emerging Trends and Applications in Computer Science.

[3]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[4]  Chien-Cheng Tseng,et al.  Computation of fractional derivatives using Fourier transform and digital FIR differentiator , 2000, Signal Process..

[5]  Chrysostomos L. Nikias,et al.  Fast estimation of the parameters of alpha-stable impulsive interference , 1996, IEEE Trans. Signal Process..

[6]  Shantanu Das,et al.  A new Fractional Fourier transform based design of a band-pass FIR filter for power feedback in nuclear reactors under noisy environment , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[7]  Chrysostomos L. Nikias,et al.  Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics , 1996, IEEE Trans. Signal Process..

[8]  Benoit B. Mandelbrot,et al.  Some noises with I/f spectrum, a bridge between direct current and white noise , 1967, IEEE Trans. Inf. Theory.

[9]  Panayiotis G. Georgiou,et al.  Alpha-Stable Modeling of Noise and Robust Time-Delay Estimation in the Presence of Impulsive Noise , 1999, IEEE Trans. Multim..

[10]  T. Söderström,et al.  Optimal instrumental variable estimates of the AR parameters of an ARMA process , 1984, The 23rd IEEE Conference on Decision and Control.

[11]  Shantanu Das,et al.  Identification of the core temperature in a fractional order noisy environment for thermal feedback in nuclear reactors , 2011, IEEE Technology Students' Symposium.

[12]  H. Vincent Poor,et al.  Signal detection in fractional Gaussian noise , 1988, IEEE Trans. Inf. Theory.

[13]  Ying Luo,et al.  Fractional order ultra low-speed position servo: improved performance via describing function analysis. , 2011, ISA transactions.

[14]  B. Mandelbrot,et al.  Fractional Brownian Motions, Fractional Noises and Applications , 1968 .

[15]  YangQuan Chen,et al.  AN OVERVIEW OF FRACTIONAL ORDER SIGNAL PROCESSING (FOSP) TECHNIQUES , 2007 .

[16]  Haiyang Chao,et al.  Roll-channel fractional order controller design for a small fixed-wing unmanned aerial vehicle , 2010 .

[17]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[18]  Chrysostomos L. Nikias,et al.  Maximum likelihood localization of sources in noise modeled as a stable process , 1995, IEEE Trans. Signal Process..

[19]  Suman Saha,et al.  Design of a Fractional Order Phase Shaper for Iso-Damped Control of a PHWR Under Step-Back Condition , 2010, IEEE Transactions on Nuclear Science.

[20]  Suman Saha,et al.  On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes , 2011, ISA transactions.