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Creators/Authors contains: "Vamvoudakis, Kyriakos G"

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  1. ; (, IEEE)
    Free, publicly-accessible full text available July 8, 2026
  2. ; (, IEEE)
    Free, publicly-accessible full text available July 8, 2026
  3. ; ; (, IEEE)
    Free, publicly-accessible full text available July 8, 2026
  4. ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available July 8, 2026
  5. ; ; (, IEEE)
    Free, publicly-accessible full text available December 16, 2025
  6. ; ; ; (, IEEE Open Journal of Control Systems)
    Free, publicly-accessible full text available January 1, 2026
  7. ; ; ; (, IEEE)
    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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    Free, publicly-accessible full text available December 16, 2025
  8. ; ; ; (, IEEE)
    Free, publicly-accessible full text available December 16, 2025
  9. ; ; ; ; ; (, IEEE Transactions on Neural Networks and Learning Systems)
    Free, publicly-accessible full text available January 1, 2026
  10. ; ; ; (, Automatica)
    Full Text Available