Event-Triggered Distributed Intelligent Learning Control of Six-Rotor UAVs Under FDI Attacks

Aiming at the six-rotor unmanned aerial vehicles subjected to false data injection attacks, an event-triggered-based distributed intelligent learning control strategy is proposed in this paper. The reinforcement learning algorithm which is designed based on neural networks is introduced to achieve intelligent optimal control. Under the actor-critic-identifier frame, three kinds of neural networks are used to realize the control action, evaluate the system performance, and estimate the unknown dynamic, respectively. Then, an improved event-triggered strategy including a decreasing function of the consensus error is designed to reduce the waste of resources while decreasing the adverse impact on tracking control performance as far as possible, which also does not exhibit Zeno behavior. Furthermore, an adaptive compensation control scheme is given, which can effectively compensate for the negative impacts brought by the false data injection attacks existing in information interaction among UAVs. Despite the impact of lumped disturbances and false data injection attacks, the proposed disturbance-observer-based distributed intelligent learning control strategy can still guarantee that all signals in the closed-loop unmanned aerial vehicle systems are bounded, and all follower unmanned aerial vehicles can track the signal of the leader unmanned aerial vehicle. Finally, some simulation verification diagrams are shown to test the feasibility of the designed control strategy. IEEE