Neural Network Adaptive Sliding Mode Control for Longitudinal Attitude of Fixed-Wing UAVs
Aiming at the problems such as model uncertainty and external interference existing in the longitudinal attitude control of fixed-wing UAVs, this paper proposes an adaptive sliding mode control method based on the radial basis function neural network (RBFNN). The method utilizes RBF to approximate t...
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| Main Author: | |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Aero Weaponry
2025-06-01
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| Series: | Hangkong bingqi |
| Subjects: | |
| Online Access: | https://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2025-0025.pdf |
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| Summary: | Aiming at the problems such as model uncertainty and external interference existing in the longitudinal attitude control of fixed-wing UAVs, this paper proposes an adaptive sliding mode control method based on the radial basis function neural network (RBFNN). The method utilizes RBF to approximate the unmodeled dynamics in the system, and adjusts the weights of the neural network in real time through the designed adaptive law, to achieve effective compensation for model errors and external interference. Meanwhile, based on the Lyapunov stability theory, the sliding mode control law is designed to ensure the global stability and finite-time convergence characteristics of the closed-loop system. The simulation experiment results show that, compared with the traditional PID control and conventional sliding mode control methods, the proposed method can significantly improve the tracking accuracy and robustness of the control system in the presence of parameter perturbation and external interference, verifying the effectiveness of this method in the longitudinal attitude control of fixed-wing UAVs. |
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| ISSN: | 1673-5048 |