Frequency Determination in Control Applications: Excitation Based Approach

New algorithms for estimation of the frequencies of oscillating waveform signals are described. Model of the signals is presented in the form of linear difference equation with unknown coefficients, which define the frequencies and amplitudes. Coefficients are estimated utilizing the property of the persistence of excitation of oscillating signals. Exponentially damped and oscillating signals are described in a unified framework. A property of excitation is proved for exponentially damped signal that contains a single frequency via diagonal dominance of an information matrix. Two applications of this frequency estimation technique are considered. The first one is filtering of the wind speed signal in wind turbine control applications, and the second one is the frequency estimation of exponentially damped signal motivated by the engine knock detection applications.

[1]  Zhe Song,et al.  Anticipatory Control of Wind Turbines With Data-Driven Predictive Models , 2009, IEEE Transactions on Energy Conversion.

[2]  Alexander Stotsky,et al.  Model Based Control of Wind Turbines: Look-Ahead Approach , 2012, ROCOND.

[3]  Sevinc Sirdas,et al.  Daily wind speed harmonic analysis for Marmara region in Turkey , 2005 .

[4]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[5]  Jin Lu Advances In Internal Model Principle Control Theory , 2011 .

[6]  Jin Lu,et al.  Identification of Exponentially Damped Sinusoidal Signals , 2008 .

[7]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[8]  昌之 佐藤 The 7th IFAC Symposium on Robust Control Designに参加して(国際会議の報告) , 2012 .

[9]  Artem Kremlev,et al.  Identification of Frequency of Biased Harmonic Signal , 2007, Eur. J. Control.

[10]  Vladimir O. Nikiforov Adaptive servomechanism controller with an implicit reference model , 1997 .

[11]  Scott C. Douglas,et al.  Adaptive algorithms for the rejection of sinusoidal disturbances with unknown frequency , 1996, Autom..

[12]  Romeo Ortega,et al.  A globally convergent frequency estimator , 1999, IEEE Trans. Autom. Control..

[13]  A new frequency domain system identification method , 2011 .

[14]  Alexey A. Bobtsov,et al.  New approach to the problem of globally convergent frequency estimator , 2008 .

[15]  Riccardo Marino,et al.  Global estimation of n unknown frequencies , 2002, IEEE Trans. Autom. Control..

[16]  Kathryn E. Johnson,et al.  LIDAR-based FX-RLS feedforward control for wind turbine load mitigation , 2011, Proceedings of the 2011 American Control Conference.

[17]  A. A. Bobtsov,et al.  A robust algorithm for identification of the frequency of a sinusoidal signal , 2007 .

[18]  X. Xia Global frequency estimation using adaptive identifiers , 2002, IEEE Trans. Autom. Control..

[19]  Phillip A. Regalia,et al.  An improved lattice-based adaptive IIR notch filter , 1991, IEEE Trans. Signal Process..

[20]  Ming Hou,et al.  Parameter Identification of Sinusoids , 2012, IEEE Transactions on Automatic Control.

[21]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[22]  Tamer Basar,et al.  Analysis of Recursive Stochastic Algorithms , 2001 .

[23]  Qing Zhang,et al.  Noise analysis of an algorithm for uncertain frequency identification , 2006, IEEE Transactions on Automatic Control.

[24]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[25]  A. Stotsky Recursive trigonometric interpolation algorithms , 2010 .

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