The CONTSID toolbox for Matlab: A software support for data-based continuous-time modelling. This chapter describes the continuous-time system identification (CONTSID) toolbox for MATLAB®, which supports continuous-time (CT) transfer function and state-space model identification directly from regularly or irregularly time-domain sampled data, without requiring the determination of a discrete-time (DT) model. The motivation for developing the CONTSID toolbox was first to fill in a gap, since no software support was available to serve the cause of direct time-domain identification of continuous-time linear models but also to provide the potential user with a platform for testing and evaluating these data-based modelling techniques. The CONTSID toolbox was first released in 1999 [15]. It has gone through several updates, some of which have been reported at recent symposia [11, 12, 16]. The key features of the CONTSID toolbox can be summarised as follows: it supports most of the time-domain methods developed over the last thirty years [17] for identifying linear dynamic continuous-time parametric models from measured input/output sampled data; it provides transfer function and state-space model identification methods for single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, including both traditional and more recent approaches; it can handle irregularly sampled data in a straightforward way; it may be seen as an add-on to the system identification (SID) toolbox for MATLAB® [26]. To facilitate its use, it has been given a similar setup to the SID toolbox; it provides a flexible graphical user interface (GUI) that lets the user analyse the experimental data, identify and evaluate models in an easy way.

References in zbMATH (referenced in 26 articles , 1 standard article )

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  1. Garnier, Hugues: Direct continuous-time approaches to system identification. Overview and benefits for practical applications (2015)
  2. Garnier, Hugues; Young, Peter C.: The advantages of directly identifying continuous-time transfer function models in practical applications (2014)
  3. Padilla, Arturo; Yuz, Juan I.; Herzer, Benjamin: Continuous-time system identification of the steering dynamics of a ship on a river (2014)
  4. Colorado, Roger Miranda; Castro, Gamaliel Contreras: Closed-loop identification applied to a DC servomechanism: controller gains analysis (2013) ioport
  5. Gawthrop, Peter; Lee, Kwee-Yum; Halaki, Mark; O’Dwyer, Nicholas: Human stick balancing: an intermittent control explanation (2013)
  6. Hu, Xiao-Li; Welsh, James S.: Necessary and sufficient convergence conditions of the instrumental variable method for identification (2013)
  7. Maruta, Ichiro; Sugie, Toshiharu: Projection-based identification algorithm for grey-box continuous-time models (2013)
  8. Schorsch, Julien; Garnier, Hugues; Gilson, Marion; Young, Peter C.: Instrumental variable methods for identifying partial differential equation models (2013)
  9. Victor, Stéphane; Malti, Rachid; Garnier, Hugues; Oustaloup, Alain: Parameter and differentiation order estimation in fractional models (2013)
  10. Ouvrard, Régis; Trigeassou, Jean-Claude: On embedded FIR filter models for identifying continuous-time and discrete-time transfer functions: the RPM approach (2011)
  11. Sakai, Fumitoshi; Sugie, Toshiharu: An identification method for MIMO continuous-time systems via iterative learning control concepts (2011)
  12. Figwer, Jarosław: Continuous-time dynamic system identification with multisine random excitation revisited (2010)
  13. Ljung, Lennart; Wills, Adrian: Issues in sampling and estimating continuous-time models with stochastic disturbances (2010)
  14. Wang, Liuping; Gawthrop, Peter; Owens, David.H.; Rogers, Eric: Switched linear model predictive controllers for periodic exogenous signals (2010)
  15. Wang, Jiandong; Zheng, Wei Xing; Chen, Tongwen: Identification of linear dynamic systems operating in a networked environment (2009)
  16. Wu, Ping; Yang, Chun-Jie; Song, Zhi-Huan: Subspace identification for continuous-time errors-in-variables model from sampled data (2009)
  17. Sugie, Toshiharu: Identification of linear continuous-time systems based on iterative learning control (2008)
  18. Thil, Stéphane; Garnier, Hugues; Gilson, Marion: Third-order cumulants based methods for continuous-time errors-in-variables model identification (2008)
  19. Ekman, M.; Larsson, E.K.: Parameter estimation of continuous-time bilinear systems based on numerical integration and separable non-linear least-squares (2007)
  20. Kim, Tae-Hyoung; Sugie, Toshiharu: An iterative learning control based identification for a class of MIMO continuous-time systems in the presence of fixed input disturbances and measurement noises (2007)

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