Identification of continuous-time systems

System identification is a well-established field. It is concerned with the determination of particular models for systems that are intended for a certain purpose such as control. Although dynamical systems encountered in the physical world are native to the continuous-time domain, system identification has been based largely on discrete-time models for a long time in the past, ignoring certain merits of the native continuous-time models. Continuous-time-model-based system identification techniques were initiated in the middle of the last century, but were overshadowed by the overwhelming developments in discrete-time methods for some time. This was due mainly to the 'go completely digital' trend that was spurred by parallel developments in digital computers. The field of identification has now matured and several of the methods are now incorporated in the continuous time system identification (CONTSID) toolbox for use with Matlab. The paper presents a perspective of these techniques in a unified framework.

[1]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[2]  F. Gantmacher,et al.  Applications of the theory of matrices , 1960 .

[3]  J. E. Diamessis,et al.  A new method for determining the parameters of physical systems , 1965 .

[4]  J. E. Diamessis,et al.  On the determination of the parameters of certain nonlinear systems , 1965 .

[6]  P. Lion Rapid identification of linear and nonlinear systems. , 1967 .

[7]  A. Tether Construction of minimal linear state-variable models from finite input-output data , 1970 .

[8]  F. Fairman,et al.  Parameter Identification for Linear Time-Varying Dynamic Processes , 1970 .

[9]  Karl Johan Åström,et al.  BOOK REVIEW SYSTEM IDENTIFICATION , 1994, Econometric Theory.

[10]  Frederick W. Fairman,et al.  Parameter identification for a class of multivariable non-linear processes , 1971 .

[11]  F. Fairman,et al.  Identification in the presence of initial conditions , 1972 .

[12]  M. Corrington,et al.  Solution of differential and integral equations with Walsh functions , 1973 .

[13]  W. Wolovich Linear multivariable systems , 1974 .

[14]  G. Rao,et al.  Identification of deterministic time-lag systems , 1976 .

[15]  S. Lamba,et al.  On simplification of unstable systems using Routh approximation technique , 1978 .

[16]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part III. Extensions , 1980 .

[17]  Ganti Prasada Rao,et al.  Identification of lumped linear systems in the presence of unknown initial conditions via Poisson moment functionals , 1980 .

[18]  Identification of lumped systems Identification of lumped linear time-varying parameter systems via Poisson moment functionals , 1980 .

[19]  Peter Young,et al.  Parameter estimation for continuous-time models - A survey , 1979, Autom..

[20]  Ganti Prasada Rao,et al.  Identification of lumped linear systems in the presence of small unknown delays-the Poisson moment functional approach , 1981 .

[21]  Amit Bhaya,et al.  A Microprocessor-Based System for On-Line Parameter Identification in Continuous Dynamical Systems , 1982, IEEE Transactions on Industrial Electronics.

[22]  Ganti Prasada Rao,et al.  Structure and parameter identification in linear continuous lumped systems—the Poisson moment functional approach , 1982 .

[23]  Ganti Prasada Rao,et al.  Transfer function matrix identification in MIMO systems via Poisson moment functionals , 1982 .

[24]  G. P. Rao,et al.  A general algorithm for parameter identification in lumped continuous systems--The Poisson moment functional approach , 1982 .

[25]  G. Rao,et al.  Parameter identification in lumped linear continuous systems in a noisy environment via Kalman-filtered Poisson moment functionals , 1982 .

[26]  G. Rao Piecewise Constant Orthogonal Functions and Their Application to Systems and Control , 1983 .

[27]  Ganti Prasada Rao,et al.  Identification of Continuous Dynamical Systems , 1983 .

[28]  L. Ljung,et al.  Design variables for bias distribution in transfer function estimation , 1986, The 23rd IEEE Conference on Decision and Control.

[29]  A. Pearson,et al.  On the identification of polynomial input-output differential systems , 1985 .

[30]  Spyros G. Tzafestas,et al.  A decade of piecewise constant orthogonal functions in systems and control , 1985 .

[31]  Brian D. O. Anderson,et al.  Identification of scalar errors-in-variables models with dynamics , 1985, Autom..

[32]  H. Unbehauen,et al.  Identification of continuous systems , 1987 .

[33]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[34]  R. Fletcher Practical Methods of Optimization , 1988 .

[35]  A. Pearson Least squares parameter identification of nonlinear differential I/O models , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[36]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[37]  S. Sagara,et al.  Recursive identification of transfer function matrix in continuous systems via linear integral filter , 1989 .

[38]  Heinz Unbehauen,et al.  Continuous-time approaches to system identification - A survey , 1990, Autom..

[39]  Graham C. Goodwin,et al.  Digital control and estimation : a unified approach , 1990 .

[40]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[41]  Amit Patra,et al.  Irreducible model estimation for MIMO systems , 1991 .

[42]  Joos Vandewalle,et al.  SVD-based subspace methods for multivariable continuous-time systems identification , 1991 .

[43]  Rolf Isermann,et al.  Adaptive Control Systems , 2005 .

[44]  Block pulse operator method for parameter identification problems in non-linear continuous systems , 1991 .

[45]  Peter C. Young,et al.  Identification, Estimation and Control of Continuous-Time Systems Described by Delta Operator Models , 1991 .

[46]  H. Unbehauen Adaptive Model Approaches , 1991 .

[47]  Rik Pintelon,et al.  Identification of Linear Systems: A Practical Guideline to Accurate Modeling , 1991 .

[48]  Zi‐Jiang Yang,et al.  Recursive identification algorithms for continuous systems using an adaptive procedure , 1991 .

[49]  Zi-Jiang Yang,et al.  Parameter Identification Based on the Steiglitz-McBride Method from Noisy Input-Output Data , 1992 .

[50]  Masayoshi Tomizuka,et al.  A new bias-compensating LS method for continuous system identification in the presence of coloured noise , 1992 .

[51]  Amit Patra,et al.  New class of discrete-time models for continuous-time systems , 1992 .