Measuring a linear approximation to weakly nonlinear MIMO systems

The paper addresses the problem of preserving the same LTI approximation of a nonlinear MIMO (multiple-input multiple-output) system. It is shown that when a nonlinear MIMO system is modeled by a multidimensional Volterra series, periodic noise and random multisines are equivalent excitations to the classical Gaussian noise, in a sense that they yield in the limit, as the number of the harmonics M->~, the same linear approximation to the nonlinear MIMO system. This result extends previous results derived for nonlinear SISO (single-input single-output) systems. Based upon the analysis of the variability of the measured FRF (frequency response function) due to the presence of the nonlinearities and the randomness of the excitations, a new class of equivalent input signals is proposed, allowing for a lower variance of the nonlinear FRF measurements, while the same linear approximation is retrieved.

[1]  J. F. Coales,et al.  An Introduction to the Analysis of Non-Linear Control Systems with Random Inputs , 1956 .

[2]  David Rees,et al.  Measurement and identification of gas turbine dynamics in the presence of noise and nonlinearities , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[3]  J. Schoukens,et al.  Robustness of the Related Linear Dynamic System Estimates in Cascaded Nonlinear MIMO Systems , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[4]  J. Schoukens,et al.  Cascading Wiener-Hammerstein systems , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[5]  Gene H. Golub,et al.  Matrix computations , 1983 .

[6]  Lennart Ljung,et al.  Linear approximations of nonlinear FIR systems for separable input processes , 2005, Autom..

[7]  M. Enqvist Linear models of nonlinear systems , 2005 .

[8]  Effect of distortion on the performance of non-linear control systems , 1963 .

[9]  David Rees,et al.  Frequency domain analysis of nonlinear systems driven by multiharmonic signals , 2004, IEEE Transactions on Instrumentation and Measurement.

[10]  Stephen P. Boyd,et al.  Analytical Foundations of Volterra Series , 1984 .

[11]  D. Rees,et al.  Nonlinear disturbance errors in system identification using multisine test signals , 1993 .

[12]  J. Douce,et al.  Frequency spectrum distortion of random signals in non-linear feedback systems , 1961 .

[13]  J. Schoukens,et al.  Crest-factor minimization using nonlinear Chebyshev approximation methods , 1991 .

[14]  David Rees,et al.  Nonlinear distortions and multisine signals. II. Minimizing the distortion , 2000, IEEE Trans. Instrum. Meas..

[15]  David Rees,et al.  Nonlinear distortions and multisine signals. II. Minimising the distortion , 1999, IMTC/99. Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309).

[16]  Jonathan R. Partington,et al.  Least-squares LTI approximation of nonlinear systems and quasistationarity analysis , 2004, Autom..

[17]  M. Enqvist Linear Models of Nonlinear FIR Systems with Gaussian Inputs , 2003 .

[18]  Pertti M. Mäkilä,et al.  Squared and absolute errors in optimal approximation of nonlinear systems , 2003, Autom..

[19]  M. Schetzen The Volterra and Wiener Theories of Nonlinear Systems , 1980 .

[20]  K. R. Godfrey,et al.  Pseudorandom signals for the dynamic analysis of multivariable systems , 1966 .

[21]  P. Makila,et al.  On optimal LTI approximation of nonlinear systems , 2004, IEEE Transactions on Automatic Control.

[22]  David Rees,et al.  Probing signals for measuring nonlinear Volterra kernels , 1995, Proceedings of 1995 IEEE Instrumentation and Measurement Technology Conference - IMTC '95.

[23]  J. Schoukens,et al.  Identification of linear systems in the presence of nonlinear distortions. A frequency domain approach. I. Non-parametric identification , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[24]  Yves Rolain,et al.  Identification of linear systems in the presence of nonlinear distortions , 2001, IEEE Trans. Instrum. Meas..

[25]  David Rees,et al.  Nonlinear distortions and multisine signals. I. Measuring the best linear approximation , 2000, IEEE Trans. Instrum. Meas..

[26]  Pertti M. Mäkilä,et al.  LTI modelling of NFIR systems: near-linearity and control, LS estimation and linearization , 2005, Autom..

[27]  J. Brenner,et al.  The Hadamard Maximum Determinant Problem , 1972 .

[28]  Lennart Ljung,et al.  LTI Approximations of Slightly Nonlinear Systems : Some Intriguing Examples , 2004 .

[29]  J. Schoukens,et al.  MEASUREMENT AND MODELLING OF LINEAR SYSTEMS IN THE PRESENCE OF NON-LINEAR DISTORTIONS , 2002 .

[30]  D. C. Evans,et al.  Identifying linear models of systems suffering nonlinear distortions , 1994 .

[31]  L. Ljung,et al.  Estimating nonlinear systems in a neighborhood of LTI-approximants , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[32]  David Rees,et al.  Frequency domain analysis of nonlinear distortions on linear frequency response function measurements , 2005, IEEE Transactions on Instrumentation and Measurement.

[33]  Ceri Evans,et al.  Identification of linear and nonlinear systems using multisine signals : with a gas turbine application , 1998 .

[34]  Johan Schoukens,et al.  Linear approximation of weakly nonlinear MIMO systems , 2004, IMTC 2004.

[35]  Albert H. Nuttall Theory and application of the separable class of random processes , 1958 .

[36]  Pertti M. Mäkilä,et al.  LTI approximation of nonlinear systems via signal distribution theory , 2006, Autom..

[37]  J. Douce A note on the evaluation of the response of a non-linear element to sinusoidal and random signals , 1958 .

[38]  J. Schoukens,et al.  Parametric and nonparametric identification of linear systems in the presence of nonlinear distortions-a frequency domain approach , 1998, IEEE Trans. Autom. Control..

[39]  David Rees,et al.  Measuring the best linear approximation of systems suffering nonlinear distortions: an alternative method , 2003, IEEE Trans. Instrum. Meas..

[40]  Zi-Qiang Lang,et al.  Evaluation of output frequency responses of nonlinear systems under multiple inputs , 2000 .

[41]  Pertti M. Mäkilä,et al.  On robustness in control and LTI identification: Near-linearity and non-conic uncertainty , 2006, Autom..

[42]  Rik Pintelon,et al.  Measurement of multivariable frequency response functions in the presence of nonlinear distortions-some practical aspects , 2002, IEEE Trans. Instrum. Meas..

[43]  The Equivalent Gain Matrix of a Multivariable Non-Linearity , 1969 .

[44]  J. J. Bussgang,et al.  Analysis of nonlinear systems with multiple inputs , 1974 .

[45]  D. Rees,et al.  System modelling in the presence of nonlinear distortions , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[46]  D. C. Evans,et al.  Design of test signals for identification of linear systems with nonlinear distortions , 1992 .

[47]  J. Schoukens,et al.  Accurate Estimation of Multivariable Frequency Response Functions , 1996 .

[48]  Stephen A. Billings,et al.  Spectral analysis for non-linear systems, Part II: Interpretation of non-linear frequency response functions , 1989 .

[49]  Jonathan R. Partington,et al.  On linear models for nonlinear systems , 2003, Autom..