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.

Heading is a crucial navigation parameter for high-precision flight guidance. Since the heading changes rapidly while unmanned aerial vehicles (UAVs) track great ellipse routes in polar regions, it is necessary to implement special guidance algorithms. This article presents a high-precision polar flight guidance algorithm for fixed-wing UAVs along great ellipse routes based on heading prediction. Specifically, a globally applicable definition of polar grid frame was proposed. On this basis, a novel flight guidance algorithm based on heading prediction was developed. Therein, the calculation method for grid azimuth on great ellipse routes based on the WGS-84 ellipse model was derived in detail, realizing accurate heading estimation and prediction. Subsequently, the predicted grid heading was utilized to tackle the difficulty of heading changes, enabling the UAV to predict and adjust its heading in advance. Moreover, an adaptive predicted lead-time adjustment strategy based on fuzzy decision-making was introduced to improve the prediction accuracy under challenging situations, and an enhanced particle swarm optimization algorithm was employed to determine the hyperparameters in fuzzy rules. To verify the effectiveness of the proposed algorithm, extensive simulations were operated using the Monte Carlo method, and the proposed algorithm demonstrated 3–4 times higher guidance accuracy compared to conventional algorithms.

For a class of single-input single-output fractional-order systems with time delay, this article presents a phase-compensated fractional-order proportional-integral-derivative controller, and develops a beneficial-predatory behavior-inspired sparrow search algorithm for parameter tuning. Specifically, a theoretical analysis of both time-domain and frequency-domain characteristics is conducted to qualitatively elucidate how the fractional order modulates their dynamic behaviors. Subsequently, the fractional-order proportional-integral-derivative controller is then augmented with a phase lead compensation network, forming a cascaded configuration that merges the tunability of fractional-order elements with the phase-advance characteristics of lead networks. Furthermore, a sparrow search algorithm inspired by beneficial-predatory behavior is introduced for intelligent parameter tuning. By integrating a dual-direction movement strategy, a diversity-based dynamic safety threshold mechanism, and a memory-based search mechanism, the population update rules are refined to prioritize risk avoidance for individuals in suboptimal regions. Using CEC 2022 benchmark functions, the beneficial-predatory behavior-inspired sparrow search algorithm is evaluated and compared with standard sparrow search algorithm, five classical algorithms, and four sparrow search algorithm variants. Ablation studies and Wilcoxon rank sum tests are further performed to verify the robustness of the proposed optimization algorithm. Eventually, the designed controller is tuned under four optimization criteria, and its application to fractional-order systems with large time-delay yields significantly enhanced transient response, reducing the overshoot by 41.27% and the settling time by 3.68% compared with the conventional fractional-order proportional-integral-derivative controller.

1. Active disturbance rejection control (ADRC), pioneered by Han (Han, 2009), has emerged as a powerful control framework renowned for its inherent robustness and exceptional disturbance rejection ...