IDENTIFICATION AND MODELING OF OPEN NONLINEAR DYNAMICAL SYSTEMS
Keywords:
modeling, identification, automated control, drilling process, nonlinear dynamical system.Abstract
The article dwells upon the existing methodological approaches to the identification and modeling of open nonlinear dynamic systems. The principal differences between modeling of nonlinear dynamical systems with unpredictable behavior – emergent systems are identified, the example of which is the system of automated control of the drilling process. The features associated with the identification of control systems for the parameters of the drilling process are specified. The interpretation of methodological approaches from the perspective of their possible use for automated drill management is carried out. A model of the Hammerstein class for information systems in drilling is proposed.
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References
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18 McCade S. On the use of nonlinear autoreqressiwe moving everage models for simulation and system identification / S. McCade, p.Davies, D.Seidel // American Control Conferens.– 1991. – P. 2559-2562.
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20. Barker H.A. Nonlinear system identification by pseudorandom testing / H.A. Barker // Preprints of 6th IFAC Symposium an identification and systems Parameter Estimation. Arlington, Virginia,– 1982. – Vol. 1. – P. 75-79.
21 Gallmon P.G. A comparison of two Hammerstein model identification algorithms /P.G.Gallmon // IEEE Trans Automat Control. –1976. – Vol. 21, No 1. – P. 124-126.
22 Narendra K.S. An iterative method for the identificate of nonlinear systems using a Hammerstein model / K.S.Narendra, .G.Gallmon // IEEE Trans Automat Control. – 1966. – Vol. 11, No 1. –P. 546-550.
23 Ding F. Chen T2007 Auxiliary modelbased least-squares identification methods for Flammerstein output-error systems // Syst. Control Lett. – 2007. – No 6(5). – P. 373-38.
24 Gotmare A Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model A.Gotmare, R.Patidar,N. V.George // Expert Syst. Appl. – 2015. –No 42(5) – P. 2538-2546.
25 Hafsi S, Laabidi Kiand Lahmari M. K. Identification of Wiener-Hammerstein model with multi segment piecewise-linear characteristic /S.Hafsi, Laabidi Kiand Lahmari M. K. // In: 16th IEEE Mediterranean Electrotechnical Conference
(MELECON), Tunisia, 2012. – P. 5-10.
26 Khani F. Robust model predictive control of nonlinear processes represented by Wiener or Hammerstein models F.Khani, M.Haeri // Chem.Eng. Sci. 129, 2015. – P.223-231.
27 Mao Y. Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique / Y.Mao, F Ding // Nonlinear Dyn. 79(3), 2015. – P.1745-1755.
28. Ding F., Chen T. Іddentification of Hammerstein nonlinear ARMAX systems / F.Din
2 Stoica P. On the convergence of on iterative algorithm used for Hammerstein systems identification / P.Stoica // IEEE trans. Automat. Control. 1981. – Vol. 26, No 4. – P. 967-969.
3 Anbumani K. Self-tuning minimumvariance control of nonliner systems of the Hammerstein model / K. Anbumani, L.Patnaik, I.Sarma // IEEE trans. Automat. Control. – 1981. – Vol. 26, No 4. – P. 959-961.
4 Wakamatsu H. Successive identification of Volterra type nonlinear multy-input-output system and its application to parameter estimation /H. Wakamatsu // Preprints of 6th IFAC Symposium an identification and systems Parameter Estimation.
Arlington, Virginia. – 1982. – Vol. 1. – P. 57- 62.
5 Lammers H.C. An identification methods for a combined Wiener-Hammerstein filter describing the encoding part of the cochlear system. / H.C. Lammers, H.B.Verbruggen, E. de Boer //Preprints of 5th IFAC Symposium an identification
and systems Parameter Estimation. Darmstadt. – 1979. – Vol. 1. – P.484-491.
6 Keviczky L. A self-tuning extremal controller for the generalized Hammerstein model / L. Keviczky, I. Vajk, J. Hetthessy // Preprints of 5th IFAC Symposium on Identification and System Parameter Estimation. Darmstadt. – 1979. – Vol. 2. – P. 1147-1151.
7 Billings S.A. Identification at Systems compased of linear dynamic and static nonlinear elements / S.A. Billings, S.Y. Fakhouri // Automatica.– 1982. – Vol. 18. – P. 15-26.
8 Chang F.N. A noniterative method for identification using Hammerstein model / F.N. Chang, R. Luus // IEEC trans. Automat. Control. 1971. – Vol. 16. – P. 464-468.
9 Каминская В.А. Идентификация динамических систем по дискретным наблюдениям /В.А. Каминская – Вильнюс; Монслас, 1985 –153 с.
10 Haber R. The identification of the discretetime Hammerstein model / R. Haber, L. Keviczky // Periodica Polytechnica. Electrical Engineering. – 1974. – No 1, Vol. 18. – P. 71-84.
11 Haist D.N. Nonlinear identification in the presence of correlated noise using a Hammerstein model / D.N. Haist, F.N. Chang, R. Luus // IEEC trans. Automat. Control. – 1973. – Vol. 18. –P. 552-555.
12 Льюнг Л. Идентификация систем. Теория для пользователя / Пер. с анг., под ред. Л.З.Цыпкина. – М.: Недра, ред. Физ-мат. Лит.,1991. – 432 с.
13 Каминскас В.А. Оценивание параметров дисктертных систем класса Гаммертштейна /В.А.Каминскас // Автоматика и телемеханика. –1975. – № 7. – 153-169.
14 Каминскас В.А. Идентификация нелинейных дискретных систем класса Гаммерштейна / В.А.Каминскас, Я.Ю.Яницкенс // Труды АНЛИТ ССР. – 1989. – Т. 2(135). – С. 65-76.
15 Wahlborg B. Desingn vqriables for bias distribution in transfer function estimation /B. Wahlborg, L. Ljung // IEEE Trans Automation Control. – 1986. – Vol. AC-31. – P. 164-144.
16 Solbrand G. Resursive methods for aff-line identification / G. Solbrand, A.Ahlen, L. Ljung //Int. I. Control. – 1985. – Vol. 41. – P. 177-191.
17 Huber H. Structure identification of nonlinear dynamic system survey on input/autput approaches / H.Huber, H Unbehauen // Automatic.– 1990. – Vol. 26. – P. 651-677.
18 McCade S. On the use of nonlinear autoreqressiwe moving everage models for simulation and system identification / S. McCade, p.Davies, D.Seidel // American Control Conferens.– 1991. – P. 2559-2562.
19 Pottman M. Application of general multimodel approach for identification of highly nonlinear processes a case study / M. Pottman, H. Unbehauen,D.Seborg // Int. I. Control. – 1993. –Vol.37. – P. 97-120.
20. Barker H.A. Nonlinear system identification by pseudorandom testing / H.A. Barker // Preprints of 6th IFAC Symposium an identification and systems Parameter Estimation. Arlington, Virginia,– 1982. – Vol. 1. – P. 75-79.
21 Gallmon P.G. A comparison of two Hammerstein model identification algorithms /P.G.Gallmon // IEEE Trans Automat Control. –1976. – Vol. 21, No 1. – P. 124-126.
22 Narendra K.S. An iterative method for the identificate of nonlinear systems using a Hammerstein model / K.S.Narendra, .G.Gallmon // IEEE Trans Automat Control. – 1966. – Vol. 11, No 1. –P. 546-550.
23 Ding F. Chen T2007 Auxiliary modelbased least-squares identification methods for Flammerstein output-error systems // Syst. Control Lett. – 2007. – No 6(5). – P. 373-38.
24 Gotmare A Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model A.Gotmare, R.Patidar,N. V.George // Expert Syst. Appl. – 2015. –No 42(5) – P. 2538-2546.
25 Hafsi S, Laabidi Kiand Lahmari M. K. Identification of Wiener-Hammerstein model with multi segment piecewise-linear characteristic /S.Hafsi, Laabidi Kiand Lahmari M. K. // In: 16th IEEE Mediterranean Electrotechnical Conference
(MELECON), Tunisia, 2012. – P. 5-10.
26 Khani F. Robust model predictive control of nonlinear processes represented by Wiener or Hammerstein models F.Khani, M.Haeri // Chem.Eng. Sci. 129, 2015. – P.223-231.
27 Mao Y. Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique / Y.Mao, F Ding // Nonlinear Dyn. 79(3), 2015. – P.1745-1755.
28. Ding F., Chen T. Іddentification of Hammerstein nonlinear ARMAX systems / F.Din
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Published
25.07.2018
How to Cite
Кропивницька, В. Б. (2018). IDENTIFICATION AND MODELING OF OPEN NONLINEAR DYNAMICAL SYSTEMS. Oil and Gas Power Engineering, (2(28), 89–94. Retrieved from https://nge.nung.edu.ua/index.php/nge/article/view/443
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POWER ENGINEERING, CONTROL AND DIAGNOSTICS OF OIL AND GAS COMPLEX FACILITIES
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