This paper addressed the optimal policy selection problem of attacker and sensor in cyber-physical systems (CPSs) under denial of service (DoS) attacks. Since the sensor and the attacker have opposite goals, a two-player zero-sum game is introduced to describe the game between the sensor and the attacker, and the Nash equilibrium strategies are studied to obtain the optimal actions. In order to effectively evaluate and quantify the gains, a reinforcement learning algorithm is proposed to dynamically adjust the corresponding strategies. Furthermore, security state estimation is introduced to evaluate the impact of offensive and defensive strategies on CPSs. In the algorithm, the ε-greedy policy is improved to make optimal choices based on sufficient learning, achieving a balance of exploration and exploitation. It is worth noting that the channel reliability factor is considered in order to study CPSs with multiple reasons for packet loss. The reinforcement learning algorithm is designed in two scenarios: reliable channel (that is, the reason for packet loss is only DoS attacks) and unreliable channel (the reason for packet loss is not entirely from DoS attacks). The simulation results of the two scenarios show that the proposed reinforcement learning algorithm can quickly converge to the Nash equilibrium policies of both sides, proving the availability and effectiveness of the algorithm.
This article presents an iterative learning control (ILC) approach for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. The distribution functions of actuators and sensors are chosen as delta function to produce pointwise control and pointwise measurement. A P‐type ILC law is proposed based on the iterative inputs and outputs to ensure the iterative process of the system is convergent under the ILC law. By utilizing integration by parts, triangle inequality, property of delta function, and Gronwall lemma, a sufficient condition based on an inequality constraint for the convergence analysis of the track error system is presented. Finally, the effectiveness of the proposed design method is verified by numerical simulation results.
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