Skip to main content
Top

2015 | OriginalPaper | Chapter

2. The Generalized Frequency Response Functions and Output Spectrum of Nonlinear Systems

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The computation of the GFRFs and/or output spectrum for a given nonlinear system described by NARX, NDE or Block-oriented models is a fundamental task for nonlinear analysis in the frequency domain. This chapter summarizes the results for the computation of the GFRFs and output spectrum for several frequently-used parametric models, and shows it clearly that the GFRFs are explicit functions of model parameters and can be regarded as an important extension of the transfer function concept for linear systems.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
go back to reference Alleyne A, Hedrick JK (1995) Nonlinear adaptive control of active suspensions. IEEE Trans Control Syst Technol 3(1):94–101CrossRef Alleyne A, Hedrick JK (1995) Nonlinear adaptive control of active suspensions. IEEE Trans Control Syst Technol 3(1):94–101CrossRef
go back to reference Baumgartner S, Rugh W (1975) Complete identification of a class of nonlinear systems from steady state frequency response. IEEE Trans Circuits Syst 22:753–759CrossRefMathSciNet Baumgartner S, Rugh W (1975) Complete identification of a class of nonlinear systems from steady state frequency response. IEEE Trans Circuits Syst 22:753–759CrossRefMathSciNet
go back to reference Bedrosian E, Rice SO (1971) The output properties of Volterra systems (nonlinear systems with memory) driven by harmonic and Gaussian inputs. Proc IEEE 59:1688CrossRefMathSciNet Bedrosian E, Rice SO (1971) The output properties of Volterra systems (nonlinear systems with memory) driven by harmonic and Gaussian inputs. Proc IEEE 59:1688CrossRefMathSciNet
go back to reference Billings SA, Peyton-Jones JC (1990) Mapping nonlinear integro-differential equation into the frequency domain. Int J Control 54:863–879CrossRef Billings SA, Peyton-Jones JC (1990) Mapping nonlinear integro-differential equation into the frequency domain. Int J Control 54:863–879CrossRef
go back to reference Bloemen HHJ et al (2001) Wiener model identification and predictive control for dual composition control of a distillation column. J Process Control 11:601–620CrossRef Bloemen HHJ et al (2001) Wiener model identification and predictive control for dual composition control of a distillation column. J Process Control 11:601–620CrossRef
go back to reference Boyd S, Chua L (1985) Fading memory and the problem of approximating nonlinear operators with Volterra series. IEEE Trans Circuits Syst CAS-32(11):1150–1160CrossRefMathSciNet Boyd S, Chua L (1985) Fading memory and the problem of approximating nonlinear operators with Volterra series. IEEE Trans Circuits Syst CAS-32(11):1150–1160CrossRefMathSciNet
go back to reference Chen S, Billings SA, Luo W (1989) Orthogonal least squares methods and their application to non-linear system identification. Int J Control 50:1873–1896CrossRefMATHMathSciNet Chen S, Billings SA, Luo W (1989) Orthogonal least squares methods and their application to non-linear system identification. Int J Control 50:1873–1896CrossRefMATHMathSciNet
go back to reference Crama P, Schoukens J (2001) Initial estimates of Wiener and Hammerstein systems using multisine excitation. IEEE Trans Instrum Meas 50:1791–1795CrossRef Crama P, Schoukens J (2001) Initial estimates of Wiener and Hammerstein systems using multisine excitation. IEEE Trans Instrum Meas 50:1791–1795CrossRef
go back to reference Gelb A, Vander Velde WE (1968) Multiple-input describing functions and nonlinear system design. McGraw-Hill, New York, NYMATH Gelb A, Vander Velde WE (1968) Multiple-input describing functions and nonlinear system design. McGraw-Hill, New York, NYMATH
go back to reference Huang A, Tanskanen JMA, Hartimo IO (1998) Design of optimum power estimator based on Wiener model applied to mobile transmitter power control. Proc IEEE Int Symp Circuits Syst 5:249–252 Huang A, Tanskanen JMA, Hartimo IO (1998) Design of optimum power estimator based on Wiener model applied to mobile transmitter power control. Proc IEEE Int Symp Circuits Syst 5:249–252
go back to reference Hunter IW, Korenberg MJ (1986) The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biol Cybern 55:135–144MATHMathSciNet Hunter IW, Korenberg MJ (1986) The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biol Cybern 55:135–144MATHMathSciNet
go back to reference Jing XJ (2011) Frequency domain analysis and identification of block-oriented nonlinear systems. J Sound Vib 330(22):5427–5442CrossRef Jing XJ (2011) Frequency domain analysis and identification of block-oriented nonlinear systems. J Sound Vib 330(22):5427–5442CrossRef
go back to reference Jing XJ, Lang ZQ (2009a) On the generalized frequency response functions of Volterra systems. Trans ASME J Dyn Syst Meas Control 131(6) Jing XJ, Lang ZQ (2009a) On the generalized frequency response functions of Volterra systems. Trans ASME J Dyn Syst Meas Control 131(6)
go back to reference Jing XJ, Lang ZQ, Billings SA, Tomlinson GR (2006) The parametric characteristic of frequency response functions for nonlinear systems. Int J Control 79(12):1552–1564CrossRefMATHMathSciNet Jing XJ, Lang ZQ, Billings SA, Tomlinson GR (2006) The parametric characteristic of frequency response functions for nonlinear systems. Int J Control 79(12):1552–1564CrossRefMATHMathSciNet
go back to reference Jing XJ, Lang ZQ, Billings SA (2007a) New bound characteristics of NARX model in the frequency domain. Int J Control 80(1):140–149CrossRefMATHMathSciNet Jing XJ, Lang ZQ, Billings SA (2007a) New bound characteristics of NARX model in the frequency domain. Int J Control 80(1):140–149CrossRefMATHMathSciNet
go back to reference Jing XJ, Lang ZQ, Billings SA (2008b) Magnitude bounds of generalized frequency response functions for nonlinear Volterra systems described by NARX model. Automatica 44:838–845CrossRefMATHMathSciNet Jing XJ, Lang ZQ, Billings SA (2008b) Magnitude bounds of generalized frequency response functions for nonlinear Volterra systems described by NARX model. Automatica 44:838–845CrossRefMATHMathSciNet
go back to reference Jing XJ, Lang ZQ, Billings SA (2008c) Frequency domain analysis for nonlinear Volterra systems with a general nonlinear output function. Int J Control 81(2):235–251CrossRefMATHMathSciNet Jing XJ, Lang ZQ, Billings SA (2008c) Frequency domain analysis for nonlinear Volterra systems with a general nonlinear output function. Int J Control 81(2):235–251CrossRefMATHMathSciNet
go back to reference Jing XJ, Lang ZQ, Billings SA (2008d) Output frequency response function based analysis for nonlinear Volterra systems. Mech Syst Signal Process 22:102–120CrossRef Jing XJ, Lang ZQ, Billings SA (2008d) Output frequency response function based analysis for nonlinear Volterra systems. Mech Syst Signal Process 22:102–120CrossRef
go back to reference Jing XJ, Lang ZQ, Billings SA (2008e) Mapping from parametric characteristics to generalized frequency response functions of nonlinear systems. Int J Control 81(7):1071–1088CrossRefMATHMathSciNet Jing XJ, Lang ZQ, Billings SA (2008e) Mapping from parametric characteristics to generalized frequency response functions of nonlinear systems. Int J Control 81(7):1071–1088CrossRefMATHMathSciNet
go back to reference Jing XJ, Lang ZQ, Billings SA (2010) Output frequency properties of nonlinear systems. Int J Nonlinear Mech 45(7):681–690CrossRef Jing XJ, Lang ZQ, Billings SA (2010) Output frequency properties of nonlinear systems. Int J Nonlinear Mech 45(7):681–690CrossRef
go back to reference Kalafatis A et al (1995) A new approach to the identification of pH processes based on the Wiener model. Chem Eng Sci 50(23):3693–3701CrossRef Kalafatis A et al (1995) A new approach to the identification of pH processes based on the Wiener model. Chem Eng Sci 50(23):3693–3701CrossRef
go back to reference Kay SM, Nagesha V (1994) Maximum likelihood estimation of signals in autoregressive noise. IEEE Trans Signal Process 42:88–101CrossRef Kay SM, Nagesha V (1994) Maximum likelihood estimation of signals in autoregressive noise. IEEE Trans Signal Process 42:88–101CrossRef
go back to reference Korenberg MJ (1982) Statistical identification of parallel cascades of linear and nonlinear systems. IFAC Symposium on System Identification Parameter Estimation 1:580–585 Korenberg MJ (1982) Statistical identification of parallel cascades of linear and nonlinear systems. IFAC Symposium on System Identification Parameter Estimation 1:580–585
go back to reference Krzyzak A (1996) On nonparametric estimation of nonlinear dynamic systems by the Fourier series estimate. Signal Process 52:299–321CrossRefMATH Krzyzak A (1996) On nonparametric estimation of nonlinear dynamic systems by the Fourier series estimate. Signal Process 52:299–321CrossRefMATH
go back to reference Li G, Wen C, Zheng WX, Chen Y (2011) Identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines. IEEE Trans Signal Process 59:2146–2159CrossRefMathSciNet Li G, Wen C, Zheng WX, Chen Y (2011) Identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines. IEEE Trans Signal Process 59:2146–2159CrossRefMathSciNet
go back to reference McWhorter LT, Scharf LL (1995) Nonlinear maximum likelihood estimation of autoregressive time series. IEEE Trans Signal Process 43:2909–2919CrossRef McWhorter LT, Scharf LL (1995) Nonlinear maximum likelihood estimation of autoregressive time series. IEEE Trans Signal Process 43:2909–2919CrossRef
go back to reference Nuij PWJM, Bosgra OH, Steinbuch M (2006) Higher-order sinusoidal input describing functions for the analysis of non-linear systems with harmonic responses. Mech Syst Signal Process 20:1883–1904CrossRef Nuij PWJM, Bosgra OH, Steinbuch M (2006) Higher-order sinusoidal input describing functions for the analysis of non-linear systems with harmonic responses. Mech Syst Signal Process 20:1883–1904CrossRef
go back to reference Peyton Jones JC, Billings SA (1989) Recursive algorithm for computing the frequency response of a class of nonlinear difference equation models. Int J Control 50(5):1925–1940CrossRefMATHMathSciNet Peyton Jones JC, Billings SA (1989) Recursive algorithm for computing the frequency response of a class of nonlinear difference equation models. Int J Control 50(5):1925–1940CrossRefMATHMathSciNet
go back to reference Rugh WJ (1981) Nonlinear system theory: the Volterra/Wiener approach. Johns Hopkins University Press, Baltimore, MDMATH Rugh WJ (1981) Nonlinear system theory: the Volterra/Wiener approach. Johns Hopkins University Press, Baltimore, MDMATH
go back to reference Schmidt G, Tondl A (1986) Non-linear vibrations. Cambridge University Press, CambridgeCrossRef Schmidt G, Tondl A (1986) Non-linear vibrations. Cambridge University Press, CambridgeCrossRef
go back to reference Sheng L, Chon KH (2003) Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance. IEEE Trans Signal Process 51:3020–3026CrossRefMathSciNet Sheng L, Chon KH (2003) Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance. IEEE Trans Signal Process 51:3020–3026CrossRefMathSciNet
go back to reference Swain AK, Billings SA (2001) Generalized frequency response function matrix for MIMO nonlinear systems. Int J Control 74(8):829–844CrossRefMATHMathSciNet Swain AK, Billings SA (2001) Generalized frequency response function matrix for MIMO nonlinear systems. Int J Control 74(8):829–844CrossRefMATHMathSciNet
go back to reference Wang J, Sano A, Chen T, Huang B (2010) Identification of Hammerstein systems without explicit parameterization of nonlinearity. Int J Control 82(5):937–952CrossRefMathSciNet Wang J, Sano A, Chen T, Huang B (2010) Identification of Hammerstein systems without explicit parameterization of nonlinearity. Int J Control 82(5):937–952CrossRefMathSciNet
go back to reference Zhu Y (1999) Distillation column identification for control using Wiener model. ACC Zhu Y (1999) Distillation column identification for control using Wiener model. ACC
Metadata
Title
The Generalized Frequency Response Functions and Output Spectrum of Nonlinear Systems
Authors
Xingjian Jing
Ziqiang Lang
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-12391-2_2