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Tracking control for networked control systems with DoS attacks via reinforcement learning method
  • +2
  • Jinliang Liu,
  • Yanhui Dong,
  • Lijuan Zha,
  • Xiangpeng Xie,
  • Engang Tian
Jinliang Liu
Nanjing University of Finance and Economics

Corresponding Author:[email protected]

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Yanhui Dong
Nanjing University of Finance and Economics
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Lijuan Zha
Nanjing University of Finance and Economics
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Xiangpeng Xie
Nanjing University of Posts and Telecommunications
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Engang Tian
University of Shanghai for Science and Technology
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Abstract

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.