On the closed loop identification of LPV models using instrumental variables

Tóth, Roland; Laurain, Vincent; Gilson, Marion; Garnier, Hugues

doi:10.3182/20110828-6-it-1002.01853

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method was analyzed on a fourth order moving pole system with colored noise and the advantages of direct approaches such as the possibility of capturing the true dynamical behavior of the system have been addressed. It can be finally pointed out that most of the methods presented here can be extended to the closed-loop case [Boonto and Werner (2008); Abbas and Werner (2009); Butcher et al (2008); Tóth et al (2011a)] for which the added difficulty lies in the noise corrupted input.…”

Section: Discussionmentioning

confidence: 99%

Identification of input-output LPV models

Laurain

Tóth²,

Gilson

et al. 2011

Linear Parameter-Varying System Identification

View full text Add to dashboard Cite

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of input-output LPV models

Laurain

Tóth²,

Gilson

et al. 2011

Linear Parameter-Varying System Identification

View full text Add to dashboard Cite

show abstract

“…The refined instrumental variables method, for open-loop and closed-loop LPV-IO identification has been introduced in [8] and [14], respectively. The method can deliver minimum variance and minimum bias (optimal estimate), however, it needs some iterations.…”

Section: Refined Instrumental Variables Methods Rivmentioning

confidence: 99%

“…By assuming an OE type of noise model the RIV method is simplified to the SRIV method. It is in general suboptimal method, but experiments show that it still delivers small bias and variance, moreover it can be used to identify BJ type model, see [8] and [14] for more details.…”

Section: Refined Instrumental Variables Methods Rivmentioning

confidence: 99%

“…The toolbox, LPVIOID, presented in this paper can be utilized to identify LPV inputoutput (LPV-IO) models in open-loop and closed-loop using the most recent identification techniques in the literature. These include the least squares (LS) method [4,13], the basic instrumental variables (IV) method [5,13] and the refined instrumental variables (RIV) as well as the simplified refined instrumental variables (SRIV) methods [8,14]. Note that all these techniques assume stable processes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lpvioid- A LPV Identification Toolbox for Matlab: Recent and Novel Techniques

Rabeei

Abbas

Hassan

2013

JES. Journal of Engineering Sciences

View full text Add to dashboard Cite

In this paper a system identification toolbox for MATLAB is introduced, including a user friendly graphical user interface. The toolbox is appropriate for the identification of systems in discrete-time linear parameter varying (LPV) form. Using LPVIOID0 F 1 it is possible to identify input-output models in open-loop and closed-loop settings based on experimental data. It comprises several recent LPV identification techniques. Furthermore, a novel method for identifying unstable plants in closed-loop is proposed. The toolbox is equipped with several tools for model validation. Examples for illustration are included.

show abstract