Event-Triggered Cascade Active Disturbance Rejection Control for Quadrotor Trajectory Tracking

This paper presents an event-triggered cascade active disturbance rejection control (CADRC) approach for quadrotor trajectory tracking. The proposed method decouples the system into position and velocity subsystems, with dedicated extended state observers (ESOs) and controllers designed for each subsystem to achieve accurate estimation and compensation of system nonlinearities, coupling effects, and external disturbances. To further optimize computational resource utilization, a hybrid event-triggering mechanism incorporating both static and dynamic thresholds is developed. This mechanism updates system states and control commands only at triggering instants, significantly reducing computational overhead. Additionally, an online self-tuning method based on particle swarm optimization (PSO) is introduced to enhance controller adaptability. Simulation results validate the robustness and efficiency of the proposed approach.