In this paper, we propose two recursive algorithms for closed loop identification under the framework of a tailor made parameterization. The closed loop transfer function is parameterized using the parameters of the open loop plant model, and utilizing knowledge of the feedback controller. When the plant model and feedback controller are all polynomial forms, a recursive least squares method with forgetting schemes is proposed to verify that this recursive method can be regarded as regularization least squares problem. Furthermore we also extend the tailor made parameterization method to nonlinear system and nonlinear controller, then an iterative least squares algorithm is applied to solve one nonlinear optimization problem.
| Published in | Machine Learning Research (Volume 2, Issue 1) |
| DOI | 10.11648/j.mlr.20170201.13 |
| Page(s) | 19-25 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Closed Loop Identification, Tailor Made Parameterization, Recursive Algorithm, Forgetting Schemes
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APA Style
Wang Jian-hong. (2017). Recursive Algorithms of Closed Loop Identification with a Tailor Made Parameterization. Machine Learning Research, 2(1), 19-25. https://doi.org/10.11648/j.mlr.20170201.13
ACS Style
Wang Jian-hong. Recursive Algorithms of Closed Loop Identification with a Tailor Made Parameterization. Mach. Learn. Res. 2017, 2(1), 19-25. doi: 10.11648/j.mlr.20170201.13
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Download@article{10.11648/j.mlr.20170201.13,
author = {Wang Jian-hong},
title = {Recursive Algorithms of Closed Loop Identification with a Tailor Made Parameterization},
journal = {Machine Learning Research},
volume = {2},
number = {1},
pages = {19-25},
doi = {10.11648/j.mlr.20170201.13},
url = {https://doi.org/10.11648/j.mlr.20170201.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170201.13},
abstract = {In this paper, we propose two recursive algorithms for closed loop identification under the framework of a tailor made parameterization. The closed loop transfer function is parameterized using the parameters of the open loop plant model, and utilizing knowledge of the feedback controller. When the plant model and feedback controller are all polynomial forms, a recursive least squares method with forgetting schemes is proposed to verify that this recursive method can be regarded as regularization least squares problem. Furthermore we also extend the tailor made parameterization method to nonlinear system and nonlinear controller, then an iterative least squares algorithm is applied to solve one nonlinear optimization problem.},
year = {2017}
}
TY - JOUR T1 - Recursive Algorithms of Closed Loop Identification with a Tailor Made Parameterization AU - Wang Jian-hong Y1 - 2017/03/02 PY - 2017 N1 - https://doi.org/10.11648/j.mlr.20170201.13 DO - 10.11648/j.mlr.20170201.13 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 19 EP - 25 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20170201.13 AB - In this paper, we propose two recursive algorithms for closed loop identification under the framework of a tailor made parameterization. The closed loop transfer function is parameterized using the parameters of the open loop plant model, and utilizing knowledge of the feedback controller. When the plant model and feedback controller are all polynomial forms, a recursive least squares method with forgetting schemes is proposed to verify that this recursive method can be regarded as regularization least squares problem. Furthermore we also extend the tailor made parameterization method to nonlinear system and nonlinear controller, then an iterative least squares algorithm is applied to solve one nonlinear optimization problem. VL - 2 IS - 1 ER -
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