Affiliation:
1. School of Aeronautics and Astronautics, Sun Yat-Sen University, Shenzhen 518107, China
2. China Academy of Launch Vehicle Technology, Beijing 100076, China
3. Science and Technology on Space Physics Laboratory, Beijing 100076, China
Abstract
This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing.
Funder
National Natural Science Foundation of China
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Cited by
2 articles.
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1. Neural network observer-based predefined-time attitude control for morphing hypersonic vehicles;Aerospace Science and Technology;2024-09
2. A High-order Model-free Adaptive Control Method for Morphing Vehicles;Proceedings of the 2024 3rd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence;2024-05-17