A dual-event-triggered adaptive neural network control for resource-constrained UAV
This paper proposes a dual-event-triggered adaptive neural-network control strategy for pose regulation of resource-constrained quadrotor unmanned aerial vehicles (UAVs). The proposed method addresses the difficulty of compensating for model uncertainties and external disturbances while optimizing communication and computational resources under limited bandwidth and onboard processing capability. In the developed framework, state information is transmitted from the UAV to the controller only when the prescribed triggering conditions are violated, and control commands are updated only when their deviation from the previously transmitted commands exceeds a prescribed threshold. This dual-event-triggered framework reduces communication and computation from both the state-transmission and control-update channels. In addition, a trigger-interval-weighted neural-network updating law is introduced to improve the applicability of the adaptive mechanism under different operating conditions. Based on Lyapunov stability theory, it is shown that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is excluded. Simulations under both constant and time-varying reference signals verify the effectiveness of the proposed method in terms of control performance and communication–computation trade-off.