The CONTSID Toolbox: A Matlab Toolbox for Continuous-Time System Identification

Garnier, Hugues; Mensler, M.

doi:10.1016/s1474-6670(17)39859-2

Cited by 42 publications

(19 citation statements)

References 2 publications

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“…2) consists in taking the derivatives C (i) ω,N (t, τ ), i ∈ {1, 2, 3, 4, 5} of the BC-NK (13), then in identifying the terms C ω,N |i,j , with j ∈ {1, 2, 3, 4, 5, 6} (see (14) and (15)), and finally in computing C ω,N |i,j (t, t) to form the E i (t) matrices (see (18)) needed for the implementation of the auxiliary signal generation system G u,y→ze (see (17)). …”

Section: Simulation Resultsmentioning

confidence: 99%

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Non-Asymptotic Kernel-Based Parametric Estimation of Continuous-time

Assalone

Lovera

et al. 2015

IEEE Trans. Automat. Contr.

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Section: Simulation Resultsmentioning

confidence: 99%

“…This method is well suited for digital implementation and has been included in the CONTSID toolbox ( [17], [18]). …”

Section: A Comparative Analysismentioning

confidence: 99%

Non-Asymptotic Kernel-Based Parametric Estimation of Continuous-time

Assalone

Lovera

et al. 2015

IEEE Trans. Automat. Contr.

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“…It is the purpose of the current contribution to discuss such issues. Compare also with other identification software packages like (Van Overschee et al 1994), (Larimore 1997), (Landau 1990), (Garnier and Mensler 2000), and (Kollar 1994). …”

Section: Introductionmentioning

confidence: 95%

Educational Aspects of Identification Software user Interfaces

Ljung

2003

IFAC Proceedings Volumes

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Apparently many users of identification techniques learn the topic only via use of commercial software. This may or may not include the software manual. This means that the software user interface -graphical or not -plays a major role in teaching identification theory and methodology to large number of users. This contribution deals with such educational / pedagogical aspects software user interfaces. In particular we focus on issues to hide certain design variables as defaults, and what can be done in case no defaults are obvious. Other questions are how to force the user to appreciate and understand the quality of an identified model, and to know what optional design choices and methods that are available, in particular if there is no Graphical User Interface (GUI).

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“…direct methods [7][8][9]. A basic difficulty is that standard approaches require the time-derivatives of I/O data in the presence of measurement noise.…”

Section: Introductionmentioning

confidence: 99%

An identification method for MIMO continuous‐time systems via iterative learning control concepts

Sakai

Sugie

2011

Asian Journal of Control

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This paper presents an identification method based on an iterative learning control (ILC) concept for the multivariable continuous-time system. For this purpose a projection type ILC, which updates the input in an appropriate parameter space, is extended to the case of multivariable systems. The robustness against measurement noise is achieved through both projection of continuous-time I/O signals onto a finite dimensional space and noise tolerant learning algorithms. Finally, a numerical example is given to demonstrate how the parameter estimation can be achieved through the proposed identification method in the presence of heavy measurement noises.

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The CONTSID Toolbox: A Matlab Toolbox for Continuous-Time System Identification

Cited by 42 publications

References 2 publications

Non-Asymptotic Kernel-Based Parametric Estimation of Continuous-time

Non-Asymptotic Kernel-Based Parametric Estimation of Continuous-time

Educational Aspects of Identification Software user Interfaces

An identification method for MIMO continuous‐time systems via iterative learning control concepts

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