Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912113356701696 |
|---|---|
| author | Gao, Weinan Jiang, Zhong-Ping Chai, Tianyou |
| author_facet | Gao, Weinan Jiang, Zhong-Ping Chai, Tianyou |
| contents | In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach Gao, Weinan Jiang, Zhong-Ping Chai, Tianyou Systems and Control In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty. |
| title | Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2411.06689 |