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 37 articles , 1 standard article )

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  1. Huang, Chao: A combined invariant-subspace and subspace identification method for continuous-time state-space models using slowly sampled multi-sine-wave data (2022)
  2. Forgione, Marco; Piga, Dario: Continuous-time system identification with neural networks: model structures and fitting criteria (2021)
  3. González, Rodrigo A.; Rojas, Cristian R.; Pan, Siqi; Welsh, James S.: Consistent identification of continuous-time systems under multisine input signal excitation (2021)
  4. Qian, Tao; Wang, Xiaoyin; Zhang, Liming: MIMO frequency domain system identification using matrix-valued orthonormal functions (2021)
  5. Piga, Dario: Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators (2020)
  6. Abdalmoaty, Mohamed Rasheed-Hilmy; Hjalmarsson, Håkan: Linear prediction error methods for stochastic nonlinear models (2019)
  7. Pascu, Valentin; Garnier, Hugues; Ljung, Lennart; Janot, Alexandre: Benchmark problems for continuous-time model identification: design aspects, results and perspectives (2019)
  8. Zhou, Bonan; Speyer, Jason L.: (\mathcalH_2) control of SISO fractional order systems (2019)
  9. Zhou, Bonan; Speyer, Jason L.: Fractional linear quadratic regulators using Wiener-Hopf spectral factorization (2019)
  10. Abrashov, Sergey; Malti, Rachid; Moreau, Xavier; Moze, Mathieu; Aioun, François; Guillemard, Franck: Optimal input design for continuous-time system identification (2018)
  11. Chen, Fengwei; Garnier, Hugues; Gilson, Marion; Zhuan, Xiangtao: Frequency domain identification of continuous-time output-error models with time-delay from relay feedback tests (2018)
  12. Garnier, Hugues: Direct continuous-time approaches to system identification. Overview and benefits for practical applications (2015)
  13. Garnier, Hugues; Young, Peter C.: The advantages of directly identifying continuous-time transfer function models in practical applications (2014)
  14. Padilla, Arturo; Yuz, Juan I.; Herzer, Benjamin: Continuous-time system identification of the steering dynamics of a ship on a river (2014)
  15. Colorado, Roger Miranda; Castro, Gamaliel Contreras: Closed-loop identification applied to a DC servomechanism: controller gains analysis (2013) ioport
  16. Gawthrop, Peter; Lee, Kwee-Yum; Halaki, Mark; O’Dwyer, Nicholas: Human stick balancing: an intermittent control explanation (2013)
  17. Hu, Xiao-Li; Welsh, James S.: Necessary and sufficient convergence conditions of the instrumental variable method for identification (2013)
  18. Maruta, Ichiro; Sugie, Toshiharu: Projection-based identification algorithm for grey-box continuous-time models (2013)
  19. Schorsch, Julien; Garnier, Hugues; Gilson, Marion; Young, Peter C.: Instrumental variable methods for identifying partial differential equation models (2013)
  20. Victor, Stéphane; Malti, Rachid; Garnier, Hugues; Oustaloup, Alain: Parameter and differentiation order estimation in fractional models (2013)

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