A Multi-Stage Optimized Adaptive Control Framework for Quadcopter Trajectory Tracking Under Wind Disturbances and Payload Variations

This paper proposes a multi-stage control strategy for quadcopter trajectory tracking under wind disturbances and payload variations. The approach is built on a standard cascaded architecture, where the outer loop regulates horizontal position and generates attitude references, while the inner loop tracks the commanded attitudes. First, Particle Swarm Optimization (PSO) is implemented offline to tune baseline PID controllers for the outer-loop horizontal channels and inner-loop attitude channels using a tracking-based objective function. Then, a supervisory fuzzy self-tuning mechanism is integrated within the inner loop to adapt the attitude PID gains online according to the tracking error and its rate, improving robustness against coupling effects and external disturbances. To ensure effective and bounded adaptation, a Genetic Algorithm (GA) is employed offline to optimize the fuzzy scaling factors. In addition, Linear Active Disturbance Rejection Control (LADRC) is assigned to the altitude channel to explicitly address payload-induced thrust-to-weight mismatch by estimating and compensating for the total disturbance. MATLAB/Simulink simulations demonstrate improved trajectory tracking and disturbance rejection compared with the PSO-tuned fixed-gain baseline. Performance evaluation using the integral of time-weighted absolute error (ITAE) confirms superior robustness under wind disturbance and payload variation scenarios while maintaining bounded control effort.