Abstract
In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates, constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics.
| Original language | English |
|---|---|
| Pages (from-to) | 763-776 |
| Number of pages | 14 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 168 |
| State | Published - 2022 |
| Event | 4th Annual Learning for Dynamics and Control Conference, L4DC 2022 - Stanford, United States Duration: 23 Jun 2022 → 24 Jun 2022 |
Keywords
- Control barrier certificates
- Data-driven controller synthesis
- Nonlinear polynomial-type systems
- Safety property