Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems
Abstract Adaptive modeling is imperative for analyzing nonlinear systems deployed in natural dynamic environments. It facilitates filtering, prediction, and automatic control of the target object in real time to respond to unpredictable and non-repetitive sudden physical impairment caused by ambient...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-025-00477-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849342977376256000 |
|---|---|
| author | Jinming Sun Yanqiu Huang Wanli Yu Alberto Garcia-Ortiz |
| author_facet | Jinming Sun Yanqiu Huang Wanli Yu Alberto Garcia-Ortiz |
| author_sort | Jinming Sun |
| collection | DOAJ |
| description | Abstract Adaptive modeling is imperative for analyzing nonlinear systems deployed in natural dynamic environments. It facilitates filtering, prediction, and automatic control of the target object in real time to respond to unpredictable and non-repetitive sudden physical impairment caused by ambient impacts, such as corrosion, thermal drift, interference, etc. Existing nonlinear modeling approaches, however, are too complex for online training or fall short in rapid model recalibration under such conditions. To address this challenge, here we present a strategy that applies a regulator to the Koopman operator, enabling real-time model adaptation for nonlinear systems. In our approach, the regulator is directly implemented in nonlinear state-space without disrupting the pre-trained black-box predictor. The proposed technique demonstrates efficacy in capturing a broad spectrum of nonlinear dynamics and exhibits rapid adaptability to system changes without requiring offline retraining. Furthermore, its lightweight implementation and high-speed performance make it well-suited for embedded systems and applications demanding fast model recalibration and robustness. |
| format | Article |
| id | doaj-art-404f03447bec4b679f4a4c9cf77bca9b |
| institution | Kabale University |
| issn | 2731-3395 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-404f03447bec4b679f4a4c9cf77bca9b2025-08-20T03:43:11ZengNature PortfolioCommunications Engineering2731-33952025-08-014111310.1038/s44172-025-00477-4Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systemsJinming Sun0Yanqiu Huang1Wanli Yu2Alberto Garcia-Ortiz3Institute of Electrodynamics and Microelectronics, University of BremenFaculty of Electrical Engineering, Mathematics and Computer Science, University of TwenteInstitute of Electrodynamics and Microelectronics, University of BremenInstitute of Electrodynamics and Microelectronics, University of BremenAbstract Adaptive modeling is imperative for analyzing nonlinear systems deployed in natural dynamic environments. It facilitates filtering, prediction, and automatic control of the target object in real time to respond to unpredictable and non-repetitive sudden physical impairment caused by ambient impacts, such as corrosion, thermal drift, interference, etc. Existing nonlinear modeling approaches, however, are too complex for online training or fall short in rapid model recalibration under such conditions. To address this challenge, here we present a strategy that applies a regulator to the Koopman operator, enabling real-time model adaptation for nonlinear systems. In our approach, the regulator is directly implemented in nonlinear state-space without disrupting the pre-trained black-box predictor. The proposed technique demonstrates efficacy in capturing a broad spectrum of nonlinear dynamics and exhibits rapid adaptability to system changes without requiring offline retraining. Furthermore, its lightweight implementation and high-speed performance make it well-suited for embedded systems and applications demanding fast model recalibration and robustness.https://doi.org/10.1038/s44172-025-00477-4 |
| spellingShingle | Jinming Sun Yanqiu Huang Wanli Yu Alberto Garcia-Ortiz Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems Communications Engineering |
| title | Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems |
| title_full | Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems |
| title_fullStr | Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems |
| title_full_unstemmed | Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems |
| title_short | Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems |
| title_sort | recursive regulator a deep learning and real time model adaptation strategy for nonlinear systems |
| url | https://doi.org/10.1038/s44172-025-00477-4 |
| work_keys_str_mv | AT jinmingsun recursiveregulatoradeeplearningandrealtimemodeladaptationstrategyfornonlinearsystems AT yanqiuhuang recursiveregulatoradeeplearningandrealtimemodeladaptationstrategyfornonlinearsystems AT wanliyu recursiveregulatoradeeplearningandrealtimemodeladaptationstrategyfornonlinearsystems AT albertogarciaortiz recursiveregulatoradeeplearningandrealtimemodeladaptationstrategyfornonlinearsystems |