Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910562665889792 |
|---|---|
| author | Zhang, Sihua Zhai, Di-Hua Dai, Xiaobing Huang, Tzu-yuan Xia, Yuanqing Hirche, Sandra |
| author_facet | Zhang, Sihua Zhai, Di-Hua Dai, Xiaobing Huang, Tzu-yuan Xia, Yuanqing Hirche, Sandra |
| contents | With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially for safety-critical control. In this paper, a learning-based control policy is proposed to ensure the safety of systems with unknown disturbances through control barrier functions (CBFs). First, the disturbance is predicted by Gaussian process (GP) regression, whose prediction performance is guaranteed by a deterministic error bound. Then, a novel control strategy using GP-based parameterized high-order control barrier functions (GP-P-HOCBFs) is proposed via a shrunk original safe set based on the prediction error bound. In comparison to existing methods that involve adding strict robust safety terms to the HOCBF condition, the proposed method offers more flexibility to deal with the conservatism and the feasibility of solving quadratic problems within the CBF framework. Finally, the effectiveness of the proposed method is demonstrated by simulations on Franka Emika manipulator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05319 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems Zhang, Sihua Zhai, Di-Hua Dai, Xiaobing Huang, Tzu-yuan Xia, Yuanqing Hirche, Sandra Systems and Control With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially for safety-critical control. In this paper, a learning-based control policy is proposed to ensure the safety of systems with unknown disturbances through control barrier functions (CBFs). First, the disturbance is predicted by Gaussian process (GP) regression, whose prediction performance is guaranteed by a deterministic error bound. Then, a novel control strategy using GP-based parameterized high-order control barrier functions (GP-P-HOCBFs) is proposed via a shrunk original safe set based on the prediction error bound. In comparison to existing methods that involve adding strict robust safety terms to the HOCBF condition, the proposed method offers more flexibility to deal with the conservatism and the feasibility of solving quadratic problems within the CBF framework. Finally, the effectiveness of the proposed method is demonstrated by simulations on Franka Emika manipulator. |
| title | Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2408.05319 |