Resilient Learning-Based Control Under Denial-of-Service Attacks
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914947879927808 |
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| author | Chakraborty, Sayan Gao, Weinan Vamvoudakis, Kyriakos G. Jiang, Zhong-Ping |
| author_facet | Chakraborty, Sayan Gao, Weinan Vamvoudakis, Kyriakos G. Jiang, Zhong-Ping |
| contents | In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07766 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resilient Learning-Based Control Under Denial-of-Service Attacks Chakraborty, Sayan Gao, Weinan Vamvoudakis, Kyriakos G. Jiang, Zhong-Ping Systems and Control In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart. |
| title | Resilient Learning-Based Control Under Denial-of-Service Attacks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2409.07766 |