Applications of Approximate Stochastic Sampled-Data Models

This chapter explores several applications of approximate stochastic sampled-data models. It is shown that, in some applications, an understanding of the role of sampling zero dynamics can be crucial in obtaining accurate results. In particular, parameter and state estimation problems are considered where the use of a restricted bandwidth may be crucial to correctly account for the artifacts of sampling.

[1]  Rik Pintelon,et al.  Frequency domain maximum likelihood estimation of linear dynamic errors-in-variables models , 2007, Autom..

[2]  Graham C. Goodwin,et al.  Dual time-frequency domain system identification , 2012, Autom..

[3]  Yves Rolain,et al.  Frequency-domain Approach to Continuous-time System Identification: Some Practical Aspects , 2008 .

[4]  T. Söderström,et al.  Least squares parameter estimation of continuous-time ARX models from discrete-time data , 1997, IEEE Trans. Autom. Control..

[5]  Liuping Wang,et al.  Identification of Continuous-time Models from Sampled Data , 2008 .

[6]  Lennart Ljung,et al.  Frequency-domain identification of continuous-time ARMA models from sampled data , 2009, Autom..

[7]  Lennart Ljung,et al.  Frequency domain identification of continuous-time output error models, Part II: Non-uniformly sampled data and B-spline output approximation , 2010, Autom..

[8]  Feng Ding,et al.  Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems , 2009, Autom..

[9]  D. Brillinger.  Time series - data analysis and theory , 1981, Classics in applied mathematics.

[10]  G. Goodwin,et al.  Identification of continuous-time state-space models from non-uniform fast-sampled data , 2011 .

[11]  Torsten Söderström,et al.  Identification of continuous-time AR processes from unevenly sampled data , 2002, Autom..

[12]  Erik K. Larsson.  Limiting sampling results for continuous-time ARMA systems , 2005 .

[13]  D. Brillinger.  Fourier analysis of stationary processes , 1974 .

[14]  Graham C. Goodwin,et al.  Robust identification of continuous-time systems from sampled data , 2008 .