A review of identification in continuous-time systems

Annual Reviews in Control

Volumn 22, Issue , 1998, Pages 145-171

  Unbehauen, H ^a   Rao, G P ^b  

^a RUHR UNIVERSITY BOCHUM   (Germany)

^b Engineering Systems Division   (United Arab Emirates)

Author keywords

Bilinear systems; Continuous time systems; Hammerstein model; Hartley modulation functions; Irreducible models; Linear estimation; Markov parameters; Nonlinear systems; Orthogonal functions; System identification; Time moments

Indexed keywords

FUNCTIONS; LINEAR CONTROL SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; NONLINEAR CONTROL SYSTEMS;

CONTINUOUS TIME SYSTEMS; HAMMERSTEIN MODEL; HARTLEY MODULATION FUNCTIONS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0031634276     PISSN: 13675788     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1367-5788(98)00015-7     Document Type: Article

References (83)

1. Aström, K.J., and P. Eykhoff (1971). System identification - A survey. Automatica, 7, 123-162.
2. Bapat, V.N. (1993). Some extensions to Poisson moment functional based estimation of continuous-time models of dynamical systems. Ph.D. Thesis, Department of Electrical Engi-neering, Indian Institute of Technology, Kha-ragpur, India.
3. Billings, S.A., H.B. Jamaluddin and S. Chen (1992). Properties of neural networks with applications to modelling nonlinear dynamical systems. Intern. J. of Control, 55, 193-224.
4. Box, G.E.P. and G.W. Jenkins (1970). Time series analysis: Forecasting and control (2nd Edition), Holden Day, San Francisco, USA.
5. Chen, S., S.A. Billings and PM. Grant (1990). Nonlinear system identification using neural netwoks. Intern. J. of Control, 51, 1191-1214.
6. Chen, S., S.A. Billings and P. M. Grant (1992). Recursive hybrid algorithm for nonlinear system identification using radial basis function networks Intern. J. of Control, 55, 1051-1070.
7. Daniel-Berhe, S. and H. Unbehauen (1996). Parameter estimation of nonlinear continuous-time systems using Hartley modulating functions. In Proc. IEE Int. Conference on Control, UKACC, Exeter, 228-233.
8. Daniel-Berhe, S. and H. Unbehauen (1996). Application of the Hartley modulating functions method for the identification of the bilinear dynamics of a dc motor. In Proc. of 35th IEEE Conference on Decision and Control, Kobe, Japan, 1533-1538.
9. Datta, K.B. and B.M. Mohan (1995). Orthogonal Functions in Systems and Control World Scientific, Singapore.
10. Dhawan, R.K., A. Sahai, D.V. Nishar and G.P. Rao (1991). Recursive estimation of Markov parameters in linear continuous-time SISO systems via block-pulse functions. In Proc. of IFAC Symposium on Identification and System Parameter Estimation, Budapest, Hungary, 1495-1500.
11. Diekmann, K. and H. Unbehauen (1979). Recursive identification of multiple-input multiple-output systems. In Proc. of IFAC Symposium on Identification and System Parameter Estimation, Darmstadt, Germany, 423-429.
12. Eykhoff, P. (1974). System identification. Wiley, New York, USA.
13. Fine, N.J. (1949). On the Walsh functions (1949). Trans. Amer. Math. Soc., 65, 372-414.
14. Flunkert, H.U. (1992). Regelstrategien auf der Basis reduzierter nichtlinearer Modelle. VDI-Verlag, Düsseldorf, Germany.
15. Gantmacher, F.R. (1959). Applications of the theory of matrices. Interscience Publishers, Inc., New York, USA.
16. Garnier, H., P. Sibille, H.L. Nguyen and T. Spott (1994). A bias-compensating least-squares method for continuous-time system identification via Poisson moment functionals. In Proc. of IFAC Symposium on System Identification and Parameter Estimation, SYSID'94, Copenhagen, Denmark, 3, 675-680.
17. Garnier, H., P. Sibille and T. Spott (1994). Influence of the initial covariance matrix on recursive LS estimation of continuous models via generalized Poisson moment functionals. In Proc. of IFAC Symposium on System Identification and Parameter Estimation, SYSID'94, Copen-hagen, Denmark, 3, 669-674.
18. Gawthrop, P.J. (1987). Continuous-time self-tuning control. IEE Publication series, London, UK.
19. Goodwin, G.C., B. Ninness and V. Poor (1991). Choice of basis functions for continuous and discrete system modeling. In Proc. of IFAC Symposium on Identification and System Parameter Estimation, Budapest, Hungary, 1179-1184.
20. Goodwin, G.C., M. Gevers and D.Q. Mayne (1991) Bias and variance distribution in transfer function estimation. In Proc. of IFAC Symposium on Identification and System Parameter Estimation, Budapest, Hungary, 952-957.
21. Hartley, R.V.L. (1942). A more symmetrical Fourier analysis applied to transmission problems. Proc. Inst. Radio Engrs., 144-150.
22. Jalili, S.A., J.A. Jordan and R.D.L. Mackie (1992). Measurement of the parameters of all-pole transfer functions using shifted Hermite modulating functions. Automatica, 38, 613-617.
23. Junge, Th. F. and H. Unbehauen (1996). Off-line identification of nonlinear systems using structurally adaptive radial basis functions. In Proc. 35th IEEE Conference on Decision and Control, Kobe, Japan, 943-948.
24. Kalman, R.E. (1970). Aspects of network and systems theory. Holt, Reinhart and Winston, New York, USA.
25. Karanam, V.R., P.A. Frick and R. R. Mohler (1978) Bilinear system identification by Walsh functions. IEEE Trans. Autom. Cont., AC-23, 709-713.
26. Kortmann, M. and H. Unbehauen (1988) Two algorithms for model structure determination of nonlinear dynamic systems with application to industrial processes. In Proc. of the 8th IFAC/IFORS Symposium on Identification and System Parameter Estimation, Beijing, People's Republic of China (Pergammon).
27. Kortmann, M., K. Janiszowski and H. Unbehauen (1988). Application and comparison of different identification schemes under industrial conditions. Intern. J. of Control, 48, 2275-2296.
28. Kortmann, P. and H. Unbehauen (1996). Identification of the structure of Fuzzy Models. In Proc. of Fuzzy 96, Zittau, Germany, 36-46.
29. Kung, F.C. and D.H. Shih (1986). Analysis and identification of Hammerstein model nonlinear delay systems using block-pulse function expansion. Intern. J. of Control, 43, 139-147.
30. Küper, P. (1992). Identifikation kontinuierlicher dynamischer Systeme mit Hilfe von Markov-Parameters. Studienarbeit ESR-9138, Lehrstuhl für Elektrische Steuerung and Regelung, Ruhr Universität, Bochum, Germany
31. Liu, G.P., V. Kadirkamanathan and S.A. Billings (1996), Stable sequential identification of continuous nonlinear dynamical systems by growing radial basis function networks. Intern. J. of Control, 65, 53-69.
32. Ljung, L. (1987). System iIdentification: Theory for the user. Prentice Hall, Inc., Englewood Cliffs, N.J., USA.
33. Maletinsky, V. (1979). Identification of continuous dynamical systems with spline-type modulating functions method. In Proc. 5th IFAC Symp. on Identification and System Parameter Estimation, Darmstadt, Germany, 275-281.
34. Middleton, R.H. and G.C. Goodwin (1990). Digital control and estimation: A unified approach. Prentice Hall, Inc., Englewood Cliffs, N.J., USA.
35. Mukhopadhyay, S. and G.P. Rao (1991). Integral-equation approach to joint state and parameter estimation in continuous-time MIMO systems., IEE-Proc., Part D, 138, 93-102.
36. Mukhopadhyay, S., A. Patra and G.P. Rao (1991). Irreducible model estimation for MIMO systems, Intern. J. of Control, 53, 223-253.
37. Mukhopadhyay, S., A. Patra and G.P. Rao (1992). New class of discrete-time models for continuous-time systems. Intern. J. of Control, 55, 1161-1187.
38. Narendra, K.S. and K. Parthasarathy (1990). Identification and control of dynamical systems using neural networks. IEEE Trans. on Neural Networks, 1, 4-27.
39. Norton, J.P. (1986). An introduction to identification. Academic Press, New York, USA.
40. Patra, A. (1989). General hybrid orthogonal functions and some applications in systems and control. Ph.D. Thesis, Department of Electrical Engineering, Indian Institute of Technology, Kharagpur-721302, India.
41. Patra, A. and G.P. Rao (1993). General hybrid orthogonal functions and their applications in systems and control. Internal Report, Department of Electrical Engineering, Indian Institute of Technology, Kharagpur-721302, India.
42. Patra, A. and H. Unbehauen (1995). Identification of a class of nonlinear continuous-time systems using Hartley modulating functions. Intern. J. of Control, 62, 1431-1451.
43. Patra, A. and G.P. Rao (1996).: General hybrid orthogonal functions and their application in systems and control. LNCIS Series Vol. 213, Springer-Verlag, Berlin, Germany.
44. Pearson, A.E. and F.C. Lee (1983). Time limited identification of continuous systems using trigonometric modulating functions. In Proc. of 3rd Yale Workshop on Applic. of Adap. Syst., New Haven, CT, USA, 168-173.
45. Pearson, A.E. and FC, Lee (1985). On the identification of polynomial input/output differential systems. IEEE Trans. Autom. Cont., AC-30, 778-782.
46. Pearson, A.E. and F.C. Lee (1985). Parameter identification of linear differential systems via Fourier based modulating functions. Control-Theory and Advanced Technology, 1, 239-266.
47. Pearson, A.E. (1988). Least squares parameter identification of nonlinear differential I/O models. In Proc. of 27th IEEE Conference on Decision and Control'88, Austin, USA, 1831-1835.
48. Pearson, A.E. and J.Q. Pan (1991). Frequency analysis via the method of moment function. In Proc. of 30th IEEE Conference on Decision and Control'91, Brighton, UK, 2024-2025.
49. Pearson, A.E. (1992). Explicit parameter identification for a class of nonlinear I/O differential operator models. In Proc. of 31st IEEE Conference on Decision and Control' 92, Tucson, Arizona, 3656-3660.
50. Pearson, A.E., Y. Shen and J.Q. Pan (1993). Discrete frequency formats for linear differential system identification. In Proc. of 12th IFAC World Congress, Sidney, Australia, VII, 143-148.
51. Pedrycz, W. (1984). Identification in fuzzy systems. In Proc. IEEE Trans. on Systems, Man, and Cybernetics, 14, 361-366.
52. Pottmann, M., H. Unbehauen and D.E. Seborg (1993). Application of a general multi-model approach fir identificationof highly nonlinear processes - a case study. Intern. J. of Control, 57, 97-120.
53. Rao, K.V., P.A. Frick and R.R. Mohler (1976). On bilinear system identification by Walsh functions. In Proc. of 4th IFAC Symp. on Identification and System. Parameter Estimation, Tiblisi, USSR, 3, 350-359.
54. Rao, G.P. (1983). Piecewise constant orthogonal functions and their applications to systems and control. LNCIS-Series, Vol. 55, Springer-Verlag, Berlin, Germany.
55. Rao, G.P., K. Diekmann and H. Unbehauen (1984). Parameter estimation in large-scale inter-connected systems. In Proc. of IFAC Symposium on Identification and System Parameter Estimation, Budapest, Hungary, 729-733.
56. Rao, G.P. and A.V.B. Subrahmanyam (1996). Models in generalized MA form for identification of continuous-time systems. In "Statistical methods in control and signal processing" Eds. Katayama and Sugimoto, Marcel Dekker, New York, USA.
57. Saha, D.C. and G.P. Rao (1980). Identification of lumped linear systems in the presence of unknown initial conditions via Poisson moment functionals. Intern. J. of Control, 31, 637-644.
58. Saha, D.C. and G.P. Rao (1983). Identification of continuous-time systems - A Poisson moment functional approach. LNCIS Series Vol. 56, Springer-Verlag, Berlin, Germany.
59. Saha, D.C. and S.K. Mandal (1990). Recursive least squares parameter estimation in SISO systems via Poisson moment functionals. Part 1: Open loop systems. Int. J. Systems Sciences, 21, 1205-1216.
60. Saha, D. C., B. K. Roy and V. N. Bapat (1991). Some generalizations and further uses of the Poisson moment functionals (PMF) approach to continuous-time model estimation. In Proc. of the 9th IFAC Symp. on Identification and System Parameter Estimation, Budapest, Hungary, 1324-1333.
61. Shinbrot, M., (1957). On the analysis of lLinear and nonlinear systems. Trans. ASME, 79, 547-452.
62. Sinha, N.K., G.P. Rao (Eds.) (1991). Identification of continuous-time systems: Methodology and computer implementation. Kluwer-Academic Publishers, Dordrecht, The Netherlands.
63. Söderström, T. and P. Stoica (1989). System identification. Prentice Hall, Hemel Hempstead, UK.
64. Subrahmanyam, A.V.B. and G.P. Rao (1993). Identification of continuous-time SISO systems via Markov parameter estimation. IEE-Proc., Part D, 140, 1-10.
65. Subrahmanyam A.V.B., D.C. Saha and G.P. Rao (1995). Identification of continuous-time MIMO systems via time moments. Control Theory and Advanced Technology, 10, 1359-1378.
66. Subrahmanyam, A.V.B., D.C. Saha and G.P. Rao (1995). Identification of continuous-time MIMO systems via time moments. Control-Theory and Advanced Technology, 10, 1359-1378.
67. Subrahmanyam, A.V.B., D.C. Saha and G. P. Rao (1996). Irreducible continuous model identification via Markov parameter estimation. Automatica, 32, 249-253.
68. Sugeno, M. and K. Tanaka (1991). Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy sets and Systems, 42, 315-334.
69. Takagi, T. and M. Sugeno (1985). Fuzzy Identification of Systems and its Application to Modeling and Control. Proc. IEEE Trans. on Systems, Man, and Cybernetics, 15, 116-132.
70. Tether, A. (1970). Construction of minimum state-variable models from finite input-output data. IEEE Trans. AC, 17, 427-436.
71. Tsang, K.M. and S.A. Billings (1994). Identification of continuous-time nonlinear systems using delayed state variable filters. Intern. J. of Control, 60, 159-180.
72. Unbehauen, H. and G.P. Rao (1987). Identification of continuous-time systems. North Holland, Amsterdam, The Netherlands.
73. Unbehauen, H. and G.P. Rao (1990). Continuous-time approaches to system identification - A survey. Automatica, 26, 23-35.
74. Unbehauen, H. (1995). Identification of nonlinear continuous-time systems by Hartley-transformation. In Proc. EURACO Workshop, Florence, Italy, 323-333.
75. Wahlberg, B. and L. Ljung (1986). Design variables for bias distribution in transfer function estimation. IEEE Trans. AC, 31, 134-144.
76. Wahlberg, B. (1991), Identification of resonant systems using Kautz filters. In Proc. of IEEE Conference on Decision and Control, Brighton, UK, 2005-2010.
77. Wahlberg, B. (1994). Laguerre and Kautz models. In Proc. of SYSID'94 10th IFAC Symposium on Identification and System Parameter Estimation, Copenhagen, Denmark, 1-12.
78. Wolovich, W.A. (1974). Linear multivariable systems. Springer-Verlag, New York, USA.
79. Yang, Z.J., S. Sagara and K. Wada (1993). Identification of continuous-time systems from sampled input-output data using bias eliminating techniques. Control Theory and Advanced Technology, 9, 53-75.
80. Young, P C. (1981). Parameter estimation of continuous-time models - a survey. Automa-tica, 17, 23-39.
81. Zadeh, L. A. (1962). From circuit theory to system theory. Proc. IRE, 50, 856-865.
82. Zhao, Z.Y., S. Sagara and K. Wada (1991). Bias-compensating least-squares method identification of continuous-time systems from sampled data. Intern. J. of Control, 53, 445-461.
83. Zhao, Z.Y., S. Sagara and M. Tomizuka (1992). A new bias-compensating least-squares method for continuous system identification in the presence of coloured noise. Intern. J. of Control, 56, 1441-1452.