Pareto: Fair Congestion Control With Online Reinforcement Learning

Emara, Salma; Wang, Fei; Li, Baochun; Zeyl, Timothy

doi:10.1109/tnse.2022.3185253

Cited by 13 publications

(1 citation statement)

References 31 publications

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“…The least-squares (LS) approach is used to obtain matrix N [41]. Because N ∈ R l×l is a symmetric matrix, at least l × l/2 data are needed to calculate (38).…”

Section: Controller Design Based On Rlmentioning

confidence: 99%

Tracking control for networked control systems with DoS attacks via reinforcement learning method

Liu

Dong

Zha

et al. 2023

Preprint

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This paper is concerned with the tracking control problem for a class of networked systems subject to denial-of-service (DoS) attacks using reinforcement learning methods. Taking the effects of DoS attacks into consideration, a novel value function is proposed, which considers the cost of the control input, external disturbance and tracking error. Then, using the structure of the value function, the tracking Bellman equation and Hamilton function are defined. By employing the Bellman optimality theory, the optimal control strategy and the game algebraic Riccati equation (GARE) are solved with the Hamilton function. Next, the desired tracking performance is guaranteed as the solution of the GARE is found. Furthermore, an attacks-based Q-learning algorithm is projected to find the solution to the optimal tracking problem without the system dynamics and the convergence of the Q-learning algorithm is given. Finally, the F-404 aircraft engine system is given to verify the effectiveness of the proposed control strategy.

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“…The least-squares (LS) approach is used to obtain matrix N [41]. Because N ∈ R l×l is a symmetric matrix, at least l × l/2 data are needed to calculate (38).…”

Section: Controller Design Based On Rlmentioning

confidence: 99%