التفاصيل البيبلوغرافية
| العنوان: |
Data-driven observer design for an inertia wheel pendulum with static friction |
| المؤلفون: |
L. Ecker, M. Schöberl |
| بيانات النشر: |
arXiv, 2022. |
| سنة النشر: |
2022 |
| مصطلحات موضوعية: |
Control and Systems Engineering, Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control |
| الوصف: |
An indirect data-driven state observer design approach for the inertia wheel pendulum considering static friction of the actuated inertia disc is presented. The frictional forces occurring in a real laboratory setup are characterized by the Stribeck effect as well as the transition between two different dynamic behaviors, sticking and non-sticking. These switching nonlinear dynamics are identified with various machine learning methodologies in a data-driven manner, i.e., the unsupervised separation and feature clustering of measured state trajectories into two dynamic classes, and the supervised classification of a state-dependent switching condition. The identified system with the interior switching-structure of two dynamics is combined with a moving horizon estimator. |
| DOI: |
10.48550/arxiv.2206.10266 |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d008f176996155a4fe88447d35ac0b0b |
| Rights: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....d008f176996155a4fe88447d35ac0b0b |
| قاعدة البيانات: |
OpenAIRE |