This paper investigates the trajectory tracking problem of quadrotor unmanned aerial vehicle(UAV)with unknown dynamics under deception attacks by proposing a fault-tolerant control strategy based on a zero-sum game framework and reinforcement learning.Firstly,the system's error dynamics are established based on the quadrotor UAV model and the intermediary control law.Then,within the zero-sum game framework,adversarial strategies for both control input and deception attacks are designed,with the cost function minimized to ensure effective fault-tolerant control in the presence of deception attacks.Subsequently,an actor-critic neural network algorithm based on reinforcement learning is developed to dynamically update the strategies,achieving the Nash equilibrium of the zero-sum game.Stability analysis demonstrates that all signals in the closed-loop system remain bounded under the proposed control algorithm.Finally,simulation results validate the effectiveness and adaptability of the proposed fault-tolerant trajectory tracking control algorithm based on the zero-sum game and reinforcement learning,which improves fault tolerance performance by 10%.
Quadrotor Unmanned Aerial VehicleTrajectory TrackingZero-sum GameReinforce-ment LearningDeception AttacksFault-tolerant Control
维普
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