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Identification of nonlinear systems and its application to Model Predictive Control

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This paper describes a new method for identifying and separating Volterra kernels of nonlinear control systems by use of pseudorandom M-sequence and correlation technique, and its application to model predictive control. By use this identification method, we can obtainVolterra kernels of up to 3rd order of a nonlinear system. M-sequence is applied to a nonlinear system and the crosscorrelation function between the input and output is calculated. then the crosscorrelation function includes all the crossections of Volterra kernels of the nonlinear system. The problem is how to separate these crossections from each other. This paper proposes two methods for separating these crosssections: one is the suitable selection of M-sequence and the other is amplitude variation method. This identification method is applied to nonlinear model predictive control.
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Bibliogr. 27 poz., rys., tab.
  • Kumamoto Study Center of The University of The Air, c/o Sojo University, Kumamoto, Japan
  • Faculty of Engineering, Kumamoto University, Kurokami 2-39-1, Kumamoto, Japan
  • [1] R. Babuska and H. B. Verbruggern An overview of fuzzy modeling for control. Control Engineering Practice. 4(11), (1996), 1593-1606.
  • [2] H. A. Barker and T. Pradisthayon: High-order autocorrelation functions of pseudorandom signals based on m-sequences. Proc. IEE, 117(9), (1970), 1857-1863.
  • [3] H. A. Barker, S. N. Obidegwo and T. Pradisthayon: Performance of antisymmetric pseudorandom signals in the measurement of second-order Volterra kernels by crosscorrelation. Proc. IEE, 119 (1972), 353-362.
  • [4] H. A. Barker and S. N. Obidegwo: Combined crosscorrelation method for measurement of 2nd-ordr Volterra kernels, ibid, 120(1), (1973), 114-118.
  • [5] S. A. Billings: Identification of nonlinear systems-a survey. Proc. IEE, 127(6), Pt.D.(1980), 272-285.
  • [6] S. A. Billings and S. Y. Fakhouri: Identification of nonlinear systems using correlation analysis and pseudorandom inputs. Int. J. Systems Sci., 11(3), (1980), 261-279.
  • [7] S. A. Billings and W. S. F. Voon: A prediction-error and stepwise-regression estimation algorithm for non-linear systems. Int. J. Control, 44(3), (1986), 803-822.
  • [8] S. S. Chen. S. A. Billings and P. M. Grant: Non-linear system identification using neural network. Int. J. Control, 51(6), (1990), 1191-1214.
  • [9] A. S. French and E. G. Butz: Measuring the Wiener kernels of a nonlinear system using the FFT. Int. J. Control, 17 (1973), 529-539.
  • [10] S. W. Golombs: Shift Register Sequences. Aegean Park Press, Laguna Hills, CA. 1982.
  • [11] R. J. Hooper and E. P. Gyftopoulos: On the measurement of characteristic kernels of a class of nonlinear systems. Proc. Symp. on Neutron Noise, Waves and Pulse Propagation, Univ. of Florida. Gainesville. (1966), 343-353.
  • [12] G. Hung: Introductory review-the kernel identification method (1910-1977)- review of theory, calculation, application, and interpretation. Mathematical Biosciences, 37(1977), 135-190.
  • [13] H. Kashiwagi and Sun Yeping: A method for identifying Volterra kernels on nonlinear systems and its applications. Proc. Asian Control Con/., Tokyo, Japan, (1994), 401-404.
  • [14] H. Kashiwagi: M-sequence and its applications. Shoukoudo co., Japan. 1996.
  • [15] H. Kashiwagi and L.Rong: Identification of Volterra Kernels of Nonlinear Van de Vusse Reactor. Trans. Control, Automation and Systems Engineering, 4(2), (2002), 109-113.
  • [16] H. Kashiwagi and Y. Li: Nonparameiric Nonlinear Model Predictive Control. Korean J. Chem. Eng., 21(2), (2004), 329-337.
  • [17] H. Kashiwag,. Y. Li and H. Harada: Identification of Nonlinear Systems with Applications to Model Predictive Control. Proc. MMAR2004, Miedzyzdroje, Poland,(2004). 1051-1056.
  • [18] Y. W. Lee and M. Schetzen: Measurement of the Wiener kernels of a nonlinear system by crosscorrelation. Int. J. Control. 2 (1965), 237-254.
  • [19] V. Z. Marmarelis: Error analysis and optimal estimation prodedures in identification of nonlinear Volterra systems. Automatica, 15 (1979), 161-164.
  • [20] R. K. Pearson: Nonlinear input/output modeling. J. Process Control, 5(4). (1995), 197-211.
  • [21] W. W. Peterson: Error-Correcting Codes. MIT Press, 1961.
  • [22] N. Rea: Nonlinear identification using inverse-repeat m-sequences. Proc. IEE, 117 (1970), 213-218.
  • [23] J. Sjoberg et al. Nonlinear black box modeling in system identification: a unified overview. Automatica, 31(12). (1995), 1691-1724.
  • [24] Y. Shi and K. E. Hecox: Nonlinear system identification by m-pulsc sequences: Application to brainstem auditory evoked responses. Trans. IEEE on Viomed. Eng., 38(9), (1991), 834-845.
  • [25] E. E. Sutter: A practical nonstochastic approach to nonlinear time-domain analysis. In Advanced Methods of Physiological System Modeling. V. Z. Marmarelis. Ed. Los Angeles; Univ. Southern California, (1987).
  • [26] X. Zhao and V. Z. Marmarelis: Nonlinear parametric models from Volterra kernels measurements. Malhl. Comput. Modelling. 27(5), ( 1998), 37-43.
  • [27] N. Zieler and J. Brillhart: On Primitive Trinomials(Mod 2). Inf. and Control. 13 (1968), 541-544.
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