Author
Listed:
- Ronghu Chi
- Yangchun Wei
- Wenlong Yao
- Jianmin Xing
Abstract
By considering non-repetitive uncertainties, an observer-based data-driven iterative learning control (ObDDILC) is proposed in this article for non-linear non-affine systems in the existence of non-repeatable disturbances, random initial values, and input constraints. Aiming to addressing the non-affine and non-linear characteristics of the systems, a linear data model (LDM) is constructed in iteration domain without introducing any physical interpretation but only for the purpose of the subsequent algorithm design and analysis. The iteration-varying initial values and disturbances are incorporated as a total non-repetitive uncertainty of the LDM. Both an iterative learning observer and a parameter estimator are proposed to address the total non-repetitive uncertainties and unknown parameters of the established LDM, respectively. Then, an observer-based learning control law is developed using the estimated output to compensate the impact of the non-repetitive uncertainties on the control performance, where a saturated function is employed to deal with the input constraints. The convergence of proposed ObDDILC is proved by using contraction mapping as the basic tool. All of the algorithms are designed and analysed without dependence of any model information except I/O data. The theoretical results are tested by simulations.
Suggested Citation
Ronghu Chi & Yangchun Wei & Wenlong Yao & Jianmin Xing, 2020.
"Observer-based data-driven iterative learning control,"
International Journal of Systems Science, Taylor & Francis Journals, vol. 51(13), pages 2343-2359, October.
Handle:
RePEc:taf:tsysxx:v:51:y:2020:i:13:p:2343-2359
DOI: 10.1080/00207721.2020.1793427
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:51:y:2020:i:13:p:2343-2359. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.