Robust Attitude Tracking for Fixed-Wing Unmanned Aerial Vehicles Using Improved Active Disturbance Rejection Control with Parameter Optimization

Fixed-wing unmanned aerial vehicles, with their advantages of long endurance and substantial payload capacity, are poised to be a key platform for the future low-altitude economy. However, the challenge of achieving precise attitude tracking control under unknown time-varying disturbances persists. To tackle this difficulty, this article introduces a soft-sign function-based active disturbance rejection control (SSADRC) method, and develops a hybrid grey wolf optimizer (HGWO) with balanced exploration–exploitation mechanisms for intelligent parameter tuning. Specifically, SSADRC utilizes a novel smooth nonlinear function with saturation constraints to reconstruct the nonlinear feedback controller and the extended state observer, ensuring smooth and stable control output. Subsequently, HGWO integrates the good point set-based initialization strategy, the fitness-based dynamic-weight strategy, the diversity-based adaptive-mutation strategy, and the logistic chaotic map-based survival-of-the-fittest strategy, addressing the tuning of multiple coupled parameters in SSADRC. Additionally, the SSADRC-based pitch attitude controller is designed for a fixed-wing unmanned aerial vehicle, and an HGWO and seven other swarm optimization algorithms are employed to tune the parameters. The results demonstrate that the HGWO exhibits the best convergence accuracy in the SSADRC parameter optimization task, and SSADRC illustrates better command tracking performance and state estimation accuracy than typical ADRC.