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In this letter, we present an active learningbased control method for discrete-time linear systems with unknown parameters under denial-of-service (DoS) attacks. For any DoS duration parameter, using switching systems theory and adaptive dynamic programming, an active learning-based control technique is developed. A critical DoS average dwell-time is learned from online inputstate data, guaranteeing stability of the equilibrium point of the closed-loop system in the presence of DoS attacks with average dwell-time greater than or equal to the critical DoS average dwell-time. The effectiveness of the proposed methodology is illustrated via a numerical example.
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This content will become publicly available on December 16, 2025
Resilient Learning-Based Control Under Denial-of-Service Attacks
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.
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- PAR ID:
- 10601373
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-1633-9
- Page Range / eLocation ID:
- 2487 to 2492
- Subject(s) / Keyword(s):
- Learning-Based Control Resiliency Denial-of-Service Attack
- Format(s):
- Medium: X
- Location:
- Milan, Italy
- Sponsoring Org:
- National Science Foundation
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