Work

Automated Aerial Refueling Docking Control Based on Adaptive RBFNN Dynamic Inversion

Year: 2025

Type: article

Abstract: This paper proposes a control method based on adaptive radial basis function neural networks (RBFNN) and dynamic inversion control for unmanned receiver to address the nonlinearities and uncertainties in the autonomous aerial refueling docking process. Following the principle of time-scale separation, the attitude control of the unmanned receiver is divided into fast and slow loops. A dynamic inversion method is employed to design the attitude loop controller, and an adaptive RBFNN system is designed to compensate for system model errors and external disturbances. The efficacy and stability of the designed control method were validated through simulation experiments conducted during autonomous aerial refueling docking. The results indicate that the autonomous aerial refueling docking system based on adaptive RBFNN dynamic inversion control exhibits robustness and adaptability, enabling reliable refueling operations in complex aerial environments and providing reliable support for long-duration unmanned missions.

Source: Guidance Navigation and Control

DOI: 10.1142/s2737480725500190

Authors: Zhijie Liu, Weiwen Zheng, Jun Shi, Yakun He

Institutions: University of Science and Technology Beijing, Chinese Aeronautical Establishment

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: G7618111036