Transfer Function Parameter Interval Estimation Using Recursive Least Squares in the Time and Frequency Domains

System identification, which includes parameter identification and non-parameter identification, is to estimate its mathematical model based on the input and output observation in system. This paper discusses the system identification theory and establishes a transfer function of 1/4 vehicle’s second-order vibration system model. Through the discrete transfer function, the system’s difference equation can be obtained to identify the system in two ways, RLS (recursive least squares) and RELS (extended recursive least squares). Finally, the paper makes a comparative analysis about RLS and RELS in connection with the vehicle model. The results show that RELS method is more accurate and has stronger convergence than RLS method, which provides the basis for the researching of control system’s algorithm, simulation and making control strategy.

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Transfer Function Models and the Limitations of Recursive Least Squares

Recursive Estimation and Time-Series Analysis ◽

10.1007/978-3-642-21981-8_6 ◽

2011 ◽

pp. 139-195

Author(s):

Peter C. Young

Keyword(s):

Transfer Function ◽

Least Squares ◽

Recursive Least Squares ◽

Function Models ◽

Transfer Function Models

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On the Potential of Least Squares Response Method for the Calibration of Superconducting Gravimeters

International Journal of Geophysics ◽

10.1155/2012/741729 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Mahmoud Abd El-Gelil ◽

Spiros Pagiatakis ◽

Ahmed El-Rabbany

Keyword(s):

Least Squares ◽

Time Domain ◽

Scale Factor ◽

Linear Regression Method ◽

Superconducting Gravimeters ◽

Frequency Domains ◽

The Time Domain ◽

Response Method ◽

Filter Response ◽

Time And Frequency Domains

One of the most important operating procedures after the installation of a superconducting gravimeter (SG) is its calibration. The calibration process can identify and evaluate possible time variability in the scale factor and in the hardware anti-aliasing filter response. The SG installed in Cantley, Canada is calibrated using two absolute gravimeters and the data are analysed in the time and frequency domains to estimate the SG scale factor. In the time domain, we use the weighted linear regression method whereas in the frequency domain we use the least squares response method. Rigorous statistical procedures are applied to define data disturbances, outliers, and realistic data noise levels. Using data from JILA-2 and FG5-236 separately, the scale factor is estimated in the time and frequency domains as−78.374±0.012 μGal/V and−78.403±0.075 μGal/V, respectively. The relative accuracy in the time domain is 0.015%. We cannot identify any significant periodicity in the scale factor. The hardware anti-aliasing filter response is tested by injecting known waves into the control electronics of the system. Results show that the anti-aliasing filter response is stable and conforms to the global geodynamics project standards.

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