RNP AR is an operational procedure that uses the aircraft’s airborne navigation equipment and global positioning system to guide the aircraft to take off and land, and it is an effective means to ensure the flight safety of civil aircraft at high-plateau environments. In this paper, a three-dimensional, precise guidance command generation method for performing an RNP AR approach procedure is proposed. The lateral navigation transition paths between different segments are constructed, and a lateral segment switching strategy based on the angular bisector is introduced. To illustrate the availability of the proposed algorithm, a simulation based on the RNP AR approach procedure of Jiuzhai Huanglong Airport is operated, and it can be drawn from the results that the proposed algorithms can provide lateral and vertical guidance capabilities that meet the requirements of RNP AR approach.

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.