CONTSID
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.
Keywords for this software
References in zbMATH (referenced in 26 articles , 1 standard article )
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Sorted by year (- Garnier, Hugues: Direct continuous-time approaches to system identification. Overview and benefits for practical applications (2015)
- Garnier, Hugues; Young, Peter C.: The advantages of directly identifying continuous-time transfer function models in practical applications (2014)
- Padilla, Arturo; Yuz, Juan I.; Herzer, Benjamin: Continuous-time system identification of the steering dynamics of a ship on a river (2014)
- Colorado, Roger Miranda; Castro, Gamaliel Contreras: Closed-loop identification applied to a DC servomechanism: controller gains analysis (2013) ioport
- Gawthrop, Peter; Lee, Kwee-Yum; Halaki, Mark; O’Dwyer, Nicholas: Human stick balancing: an intermittent control explanation (2013)
- Hu, Xiao-Li; Welsh, James S.: Necessary and sufficient convergence conditions of the instrumental variable method for identification (2013)
- Maruta, Ichiro; Sugie, Toshiharu: Projection-based identification algorithm for grey-box continuous-time models (2013)
- Schorsch, Julien; Garnier, Hugues; Gilson, Marion; Young, Peter C.: Instrumental variable methods for identifying partial differential equation models (2013)
- Victor, Stéphane; Malti, Rachid; Garnier, Hugues; Oustaloup, Alain: Parameter and differentiation order estimation in fractional models (2013)
- Ouvrard, Régis; Trigeassou, Jean-Claude: On embedded FIR filter models for identifying continuous-time and discrete-time transfer functions: the RPM approach (2011)
- Sakai, Fumitoshi; Sugie, Toshiharu: An identification method for MIMO continuous-time systems via iterative learning control concepts (2011)
- Figwer, Jarosław: Continuous-time dynamic system identification with multisine random excitation revisited (2010)
- Ljung, Lennart; Wills, Adrian: Issues in sampling and estimating continuous-time models with stochastic disturbances (2010)
- Wang, Liuping; Gawthrop, Peter; Owens, David.H.; Rogers, Eric: Switched linear model predictive controllers for periodic exogenous signals (2010)
- Wang, Jiandong; Zheng, Wei Xing; Chen, Tongwen: Identification of linear dynamic systems operating in a networked environment (2009)
- Wu, Ping; Yang, Chun-Jie; Song, Zhi-Huan: Subspace identification for continuous-time errors-in-variables model from sampled data (2009)
- Sugie, Toshiharu: Identification of linear continuous-time systems based on iterative learning control (2008)
- Thil, Stéphane; Garnier, Hugues; Gilson, Marion: Third-order cumulants based methods for continuous-time errors-in-variables model identification (2008)
- Ekman, M.; Larsson, E.K.: Parameter estimation of continuous-time bilinear systems based on numerical integration and separable non-linear least-squares (2007)
- 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)