TY - GEN
T1 - Adaptive Neural Network Based Prescribed Performance Sliding Mode Control for UAV Attitude Dynamics
AU - Zhang, Han
AU - Yang, Jiaxing
AU - Zheng, Xiaolong
AU - Yang, Xuebo
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - This paper proposes a prescribed performance sliding mode control method based on adaptive neural networks for attitude tracking control of quadrotor unmanned aerial vehicles(UAVs). Design a sliding mode surface with prescribed performance constraints to ensure that the error tracking is always within the prescribed performance range and dynamically map the restricted sliding mode surface to an unconstrained system through error conversion technology. On this basis, an adaptive neural network is adopted to conduct an online approximation of the uncertain terms and external disturbances of the system, reducing the dependence of traditional sliding mode control on the uncertain upper bound. According to Lyapunov criterion, the tracking error of the system is semiglobally uniformly bounded. Finally, this method is applied to the digital simulation experiment of the quadrotor UAVs control system.
AB - This paper proposes a prescribed performance sliding mode control method based on adaptive neural networks for attitude tracking control of quadrotor unmanned aerial vehicles(UAVs). Design a sliding mode surface with prescribed performance constraints to ensure that the error tracking is always within the prescribed performance range and dynamically map the restricted sliding mode surface to an unconstrained system through error conversion technology. On this basis, an adaptive neural network is adopted to conduct an online approximation of the uncertain terms and external disturbances of the system, reducing the dependence of traditional sliding mode control on the uncertain upper bound. According to Lyapunov criterion, the tracking error of the system is semiglobally uniformly bounded. Finally, this method is applied to the digital simulation experiment of the quadrotor UAVs control system.
KW - adaptive neural network
KW - attitude tracking control
KW - prescribed performance control
KW - sliding-mode control
UR - https://www.scopus.com/pages/publications/105043923939
U2 - 10.1109/CCDC69976.2026.11560367
DO - 10.1109/CCDC69976.2026.11560367
M3 - 会议稿件
AN - SCOPUS:105043923939
T3 - 38th Chinese Control and Decision Conference, CCDC 2026
SP - 4422
EP - 4427
BT - 38th Chinese Control and Decision Conference, CCDC 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Chinese Control and Decision Conference, CCDC 2026
Y2 - 15 May 2026 through 18 May 2026
ER -