Event-Triggered Based Adaptive Dynamic Surface Control for a Class of Quadrotor UAVs

A hybrid adaptive control scheme for quadrotor control system based on event-triggered mechanism is proposed for the trajectory tracking control problem of quadrotor UAV. The high-gain state observer is constructed to estimate the unmeasurable states, then the adaptive radial basis function neural networks (RBFNNs) dynamic surface control strategy is designed to achieve precise tracking control. The event-triggered mechanism is introduced, which is effective reduces the update frequency of the system control signal. The simulation results show that the proposed control scheme can achieve more accurate tracking performance than the traditional backstepping sliding mode control (BSMC) scheme without sacrificing the tracking performance of the control system.